Interactive video search tools: a detailed analysis of the video browser showdown 2015
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Interactive video retrieval tools developed over the past few years are emerging as powerful alternatives to automatic retrieval approaches by giving the user more control as well as more responsibilities. Current research tries to identify the best combinations of image, audio and text features that combined with innovative UI design maximize the tools performance. We present the last installment of the Video Browser Showdown 2015 which was held in conjunction with the International Conference on MultiMedia Modeling 2015 (MMM 2015) and has the stated aim of pushing for a better integration of the user into the search process. The setup of the competition including the used dataset and the presented tasks as well as the participating tools will be introduced . The performance of those tools will be thoroughly presented and analyzed. Interesting highlights will be marked and some predictions regarding the research focus within the field for the near future will be made.
KeywordsExploratory search Video browsing Video retrieval
The Video Browser Showdown (VBS), also known as Video Search Showcase, is an interactive video search competition where participating teams try to answer ad-hoc queries in a shared video data set as fast as possible. Typical efforts in video retrieval focus mainly on indexing and machine-based search performance, for example, by measuring precision and recall with a test data set. With video getting omnipresent in regular consumers lives, it becomes increasingly important though to also include the user into the search process. The VBS is an annual workshop at the International Conference on MultiMedia Modeling (MMM) with that goal in mind.
Researchers in the multimedia community agree that content-based image and video retrieval approaches should have a stronger focus on the user behind the retrieval application [13, 45, 50]. Instead of pursuing rather small improvements in the field of content-based indexing and retrieval, video search tools should aim at better integration of the human into the search process, focusing on interactive video retrieval [8, 9, 18, 19] rather than automatic querying.
Therefore, the main goal of the Video Browser Showdown is to push research on interactive video search tools. Interactive video search follows the idea of strong user integration with sophisticated content interaction  and aims at providing a powerful alternative to the common video retrieval approach . It is known as the interactive process of video content exploration with browsing means, such as content navigation , summarization , on-demand querying , and interactive inspection of querying results or filtered content . Contrarily to typical video retrieval, such interactive video browsing tools give more control to the user and provide flexible search features, instead of focusing on the query-and-browse-results approach. Hence, even if the performance of content analysis is not optimal, there is a chance that the user could compensate shortcomings through ingenious use of available features. This is important since it has been shown that user can give good performances even with very simple tools, e.g. a simple HTML5 video player [10, 12, 42, 44].
Other interesting approaches include using additional capturing devices such as the Kinect sensor in conjunction with human action video search , exercise learning in the field of healthcare  or interactive systems for video search . In  for example, an interactive system for human action video search based on the dynamic shape volumes is developed – the user can create video queries by posing any number of actions in front of a Kinect sensor. Of course, there are many other relevant and related tools in the fields of interactive video search, video interaction, and multimedia search, which are however out of the scope of this paper. The interested reader is referred to other surveys in this field, such as [34, 46, 47].
In this paper we provide an overview of the participating tools along with a detailed analysis of the results. Our observations highlight different aspects of the performance and provide insight into better interface development for interactive video search. Details of the data set and the participating tools are presented, as well as their achieved performance in terms of score and search time. Further, we reflect on the achieved results so far, give detailed insights on the reasons why specific tools and methods worked better or worse, and subsume the experience and observations from the perspective of the organisers. Based on this, we make several proposals for highly promising approaches to be used with future iterations of this interactive video retrieval competition.
The remainder of the paper is organized as follows. Section 2 gives a short description of the competition. Section 3 makes an overview of both the presented tasks and of the obtained results. Section 4 provides short descriptions of the participating tools. A detailed analysis of the results for visual expert rounds is presented in Section 5. The results for the textual expert round are presented in Section 6 and the ones for the novice round in Section 7. A short historical overview over the last rounds of the Video Browser Showdown together with some advice on developing interactive video search tools are given in Section 8. Section 9 concludes the paper and highlights the most important observations stemming from the competition.
2 Video browser showdown 2015
VBS 2015 was the fourth iteration of the Video Browser Showdown and took place in Sydney, Australia, in January 2015, held together with the International Conference on MultiMedia Modeling 2015 (MMM 2015). Nine teams participated in the competition and performed ten visual known-item search tasks (Expert Run 1), six textual known-item search tasks (Expert Run 2), as well as four visual and two textual known-item search tasks with non-expert users (Novice Run). The shared data set consisted of 153 video files containing about 100 hours of video content in PAL resolution (720 ×576@25p) from various BBC programs, and was a subset of the MediaEval 2013 Search & Hyperlinking data set . The size of the data set was about 32 GB; the videos were stored in webm file format and encoded with the VP8 video codec, and the Ogg Vorbis audio codec. The data were made available to the participants about two months before the event.
During the event, users interactively try to solve search tasks with the participating tools; first in a closed expert session with the developers of a respective tool, then in a public novice session with volunteers from the audience – typically experts in the field of multimedia. Search tasks are so-called known-item search tasks where users search for information that they are familiar with. For the last two years  visual and textual queries were used. These are clips or textual descriptions of 20-seconds long target segments in a moderately large shared data set (100 hours at VBS 2015), which are randomly selected on site. After a clip or the text for a task is presented, participants have to find it in the database. They are presented through a PC connected to a projector, which runs the VBS Server that presents target segments (1) through playback of the corresponding clip for visual tasks, and (2) through presentation of a static textual description of the clip – collaboratively created by the organizers – for textual queries. After presentation of the visual or textual description, the VBS Server is responsible for collecting and checking the results found by the participants and for calculating the achieved score for each team.
The tools of all teams are connected to the VBS Server, and send information about found segments (frame numbers or frame ranges) to the server via HTTP requests. The server checks if the segment was found in the correct video and at the correct position and computes a score for the team, according to a formula that considers the search time (typically a value between 4 and 8 minutes) and the number of previously submitted wrong results for the search task (see [2, 42]). According to these parameters a team can get up to 100 points for a correct solved task, and in worst case zero points for a wrong or unanswered task. The scores are summed up and the total score of each session is used to determine the winner of the session. Finally, the team with the maximum grand total score is selected as the final winner of the competition. VBS 2015 used three sessions: (1) a visual expert’s session, (2) a textual expert’s session, and (3) a visual novice’s session.
In order to focus on the interactive aspects of search and avoid focusing too much on the automatic retrieval aspects, restrictions are imposed. Retrieval tools that only use text queries without any other interaction feature are therefore not allowed. However, participants may perform textual filtering of visual concepts, or navigate through a tree of textual classifications/concepts, for example. Moreover, the Video Browser Showdown wants to foster simple tools and, therefore, perform a novice session where volunteers from the audience use the tools of the experts/developers to solve the search tasks and by doing so test the usability in an implicit way.
In 2015, the focus of the competition has further moved towards dealing with realistically sized content collections. Thus, the tasks using only single videos, that were present in the 2013 and 2014 editions, have been discontinued, and the data set has been scaled up, from about 40 hours in 2014 to about 100 hours. The competition started with expert tasks in which visual and textual queries had to be solved. Then the audience was invited to join in and the tools were presented to allow the participants to understand how the tools are used by the experts. In the next sessions members of the audience (“novices”) took over for visual and text queries, and operated the tools themselves.
Each task in each of the three sessions (visual/textual expert run, novice run) aimed at finding a 20 seconds query video, where the excerpt does not necessarily start and stop at shot or scene boundaries. For visual queries, the video clip is played once (with sound) on a large, shared screen in the room. For textual queries, experts created descriptions of the contents of the clips, which were displayed on the shared screen and read to the participants. Participants were given a maximum time limit of eight minutes to find the target sequence in the corresponding video data (note that in the 2013 and 2014 competitions, the search in single videos was limited to three minutes, while the archive tasks in the 2014 competition had a limit of six minutes).
The overall score Sk for tool k is simply the sum of the scores of all tasks of the three sessions. Equations (1) and (2) were designed in order avoid trial-and-error approaches: participants submitting several wrong results get significantly fewer points than participants submitting just one correct result. Additionally, the linear decrease of the score over time should motivate the teams to find the target sequence as fast as possible.
The hardware for the competition was not normalized; all participating teams were free to use the equipment best supporting the requirements and efficiency of their video browsers. The teams used notebook computers or tablets, depending on the respective browsing approaches.
3 VBS2015 evaluation overview
The current section aims to give a general overview of the competition’s tasks and results and to point towards some of the most interesting conclusions. A detailed analysis and discussion of the results which focuses on the different tasks types follows in Section 5.
3.1 Overview of the rounds and of presented tasks
As already mentioned in Section 2, the competition focused on two types of tasks, namely visual and textual tasks.
3.1.1 Expert run 1
Overview of the presented video targets for the visual experts round
Frame capture at
3.1.2 Expert run 2
Descriptions of the target segments provided for the text experts session
Panel of four participants with bluish background on the top (“COMEDIANS” displayed on their desk below) being asked a quiz question about a Russian exclave (i.e., separated region) in Europe, after the question is asked close-ups of the people are shown.
A man on a meadow (green grass in background), standing next to an ultralight aircraft and getting into a red and black overall.
A group of mostly kids practicing Karate moves indoors (in white clothes), including close-ups of a blond young woman talking to a girl, and shots showing the instructor, a bald man with glasses.
A prairie scenery with a hill on the left and mountains in the background, an old man with a black suit and hat walking slowly up the hill. He is first seen from behind, then a close-up of the man is shown. Then a close-up shot of a running wolf in the grass is shown.
A red/brown coloured map of Europe, with Alsace and the city of Strasbourg highlighted, showing also the surrounding countries (e.g., Germany, France). Then black/white shots of soldiers marching in a city (for several seconds). During the whole sequence a female sign language interpreter is visible in the lower right.
A BBC Four trailer, starting with a colourful huge bookshelf, then showing a sequence of countryside shots, and in each of them a yellow/gold glowing path showing music notes is appearing.
Overview of the described video targets for the text experts round
Frame capture at
3.1.3 Novice run
As already mentioned, the novice round that followed the visual and textual expert rounds, consisted of a total of six tasks, out of which four were visual tasks and two were textual tasks. They were presented as two sequences of two visual tasks followed by a textual task. Those tasks were extracted from the same pools as the tasks of the previous visual and text rounds and were in no way different.
Overview of the presented and described video targets for the novices round
rame capture at
Descriptions of the target segments provided for the text novices tasks
First a close-up of a beehive with many bees, then close-up shots of ants cutting and carrying large green leaves.
Piece about the ESA Ulysses mission, showing an image of the sun and the probe left above it, while zooming out it is explained how it orbits around the sun.
The next shot shows the sun centered in a greenish hue (“STEREO” image). The flyby of a rendered model of the probe is shown.
3.2 Overview of results
Out of the nine participating teams [3, 5, 11, 22, 27, 35, 37, 39, 51], six managed to score points during the competition. Further analysis of the logs showed that one of the three non-scoring teams managed to solve one of the tasks but submitted its data using a wrong format.
The three top ranking teams (SIRET, IMOTION and UU) together with the forth (HTW) show the most uniform increases in terms of scored points across the visual expert and novice rounds (Fig. 2). In the case of the text expert round, only the UU and NII-UIT teams show this pattern. Overall the achievements during the novice round were over those in the text expert round.
In the case of the NII-UIT team, the best scoring round was that of the novices (it actually won the round) during which the team climbed up to rank 5.
The slowest of the participants (Fig. 2) was by far the UU team which was almost 2 times slower than the for-last participant in terms of speed - the HTW team. Since the UU team presented a tool designed for human computation this does not come as a surprise. What comes as a surprise is the excellent score they achieved - rank 3 overall. Also, it is interesting to note that the UU team was slowest during the visual expert round and got faster during the text expert and novice rounds with the additional note, that during the novice round only half of the targets were found (two visual and one textual targets).
When comparing the three rounds, visual expert, textual expert and novice, the difference in speed, when it comes to finding the correct target, is not that big as when comparing experts and novices. The novices are a little bit slower except in the case of UU team, where the novices actually seem to perform faster then the experts. Unfortunately, due to the small number of novice tasks we are not able to generalize on whether this has to do with the actual tasks being presented, or this is because the novices just exploited the tools close to their full potential, as they had no false expectations. Also, as already mentioned, it is important to note that in most cases, the participants in the novice round were actually experts from the other participating teams which tested the “competition’s” tools.
From the scoring point of view we see two team clusters: one that scored over 1000 points (SIRET, IMOTION, UU and HTW) and one that score under 600 (NII-UIT and VERGE), while from the time point of view, all the teams with the exception of the UU team, had similar overall completion times for their successful submissions.
We have performed a one-way ANOVA to determine if the successful submission times for the visual experts round was different for the participating teams. Each of the IMOTION, SIRET and UU teams had one outlier. The search time was normally distributed for all interfaces, as assessed by Shapiro-Wilk’s test (p > .05). There was homogeneity of variances, as assessed by Levene’s test for equality of variances (p = .92). The search time was statistically significantly different between the interfaces, F(5, 30) = 3.045, p < .05.
4 Scoring video search tools in VBS2015
The IMOTION system  is a sketch and example-based video retrieval system. It is based on a content-based video retrieval engine called Cineast  that focuses on Query-by-Sketch and also supports Query-by-Example and motion queries.
In IMOTION, a user can specify a query by means of a sketch that may include edge information, color information, motion information, or any combination of these, or provide sample images or sample video snippets as query input. It uses multiple low-level features such as color- and edge histograms for retrieval.
The IMOTION system extends the set of features by high level visual features such as state-of-the-art convolutional neural network object detectors and motion descriptors. All feature vectors along with meta-data are stored in the database and information retrieval system ADAM  which is built upon PostgreSQL and is capable of performing efficient vector space retrieval together with Boolean retrieval.
The browser-based user interface, which is optimized to be usable with touch screen devices, pen tablets as well as a mouse, provides a sketching canvas as well as thumbnail previews of the retrieved results.
Every query sketch adjustment is projected to the results area (see the left side of Fig. 6) immediately thanks to the efficient retrieval model employed which is based on position-color feature signatures. Each row represents one matched scene delimited by the matched key-frames (marked with red margin) and accompanied by a few preceding and following key-frames from the video clip. Any displayed scene can be selected as either positive or negative example for additional filtering. Alternatively, particular colored circles might be picked from displayed key-frames to the sketches similarly to picking up a color with the eyedropper tool. Regarding the video-level exploration, users may exclude a particular video from the search or contrary, focus on a single video. Especially the later mentioned feature often led to success as its appropriate usage can significantly increase results relevancy. When exploring a single video, users may find useful the extended results row (see the bottom of Fig. 6) enriched with Interactive Navigation Summary  displaying (in this case, 5 dominant colors of each key-frame).
The HTW system  is a map-based browsing system for visually searching video clips in large collections. Based on ImageMap , it allows the user to navigate through a hierarchical-pyramid structure in which related scenes are arranged close to each other. An extended version of ImageMap can be viewed online at www.picsbuffet.com. The interaction is similar to map services like Google Maps: a view port revealing only a small portion of the entire map at a specific level. Zooming in (or out) shows more (or less) similar scenes from lower (or higher) levels. Dragging the view shows related images from the same level. While the hierarchical-pyramid of all scenes in the data set (“Map of Scenes”) has been precomputed to avoid performance issues, the map for a single video is generated on the fly and can therefore be filtered or altered based on the actions of the user.
The HTW-Berlin video browsing interface is divided into three parts: the browsing area on the left, the search result area in the middle and the search input area on the right.
In case the content of a scene is described as text or verbally, less or no visual information may be available, making a search nearly unfeasible. Usually the user still has an idea how the scene might look like. With the help of ImageMap it is possible to quickly navigate and check potential key frames.
The UU system  excludes all kinds of video analysis and machine-based query processing and relies exclusively on interaction design and human browsing abilities. Past research has demonstrated that a good and efficient interaction design can have a significant impact on search performance [24, 49] – especially when searching in single video files or small data sets. This claim is supported by previous years’ VBS results, for example, the baseline study presented in .
The VERGE system  is an interactive retrieval system that combines advanced retrieval functionalities with a user-friendly interface, and supports the submission of queries and the accumulation of relevant retrieval results. The following indexing and retrieval modules are integrated in the developed search application: a) Visual Similarity Search Module based on K-Nearest Neighbour search operating on an index of lower-dimensional PCA-projected VLAD vectors ; b) High Level Concept Detection for predefined concepts by training Support vector machines with annotated data and five local descriptors (e.g. SIFT, RGB-SIFT, SURF, ORB etc), which are compacted and aggregated using PCA and encoding; the output of the trained models is combined by means of late fusion (averaging); c) Hierarchical Clustering incorporating a generalized agglomerative hierarchical clustering process , which provides a structured hierarchical view of the video keyframes.
5 Evaluation details on visual experts tasks
5.1 Search time and achieved points
The SIRET team completed 8 out of 10 proposed tasks, while the IMOTION team successfully completed 9 of the 10 tasks. The UU and HTW teams completed 7 tasks while the NII-UIT and VERGE teams completed 2 tasks each.
Frames submitted during the visual experts round (wrong submissions have red contours; right submissions have green contours)
Frames submitted by the participant teams during the textual experts round (wrong submissions have red contours; right submissions have green contours)
The IMOTION team had a slow start during the first 2 tasks, but then submitted the correct target scene very quick for the next 7 tasks. In fact they were the quickest for task number 3, 5, 7, 8 and 9.
The same slow start during the first tasks can be seen in the case of the other teams: SIRET, HTW and UU. This might be due to an accommodation phase in which the teams got accustomed to the competition spirit as well as with the responsiveness of the various tools’ features under the on-site conditions.
In the case of the three teams that successfully completed the first two tasks IMOTION, SIRET and HTW, the time needed to complete the tasks actually increases: this can be explained either by the fact that the target scene is located “deep” within the archive and more time is needed for investigation, or by the fact that they tried to apply for the second round the same strategy they employed for the first one and failed.
For the UU team which had a tool that relied heavily on human computation, the time needed to successfully find a target scene shows the lowest variance with the exception of task 4 in which actually the UU team was the fastest (this might be due to the positioning of the target scene at the very beginning of the video and the navigation model employed).
The tasks 3, 4 and 6 were successfully completed by 5 teams; in the case of tasks 3 and 4 by the same teams configuration: IMOTION, SIRET, UU, HTW and NII-UIT. The tasks 5, 9 and 10 were completed by 4 teams, tasks 1 and 2 by 3 teams while tasks 7 and 8 were completed by only 2 teams: IMOTION and UU. While in the case of IMOTION it seems that the internals have played the most important role, since the team was fastest for exactly those two “difficult” tasks, in the case of UU it seems to be raw human power that had been rewarded - in the case of task 7 the UU team had the slowest completion time over all tasks (not in comparison with the other teams though).
5.2 Erroneous Submissions
It is interesting to note that the IMOTION and UU teams always identified the correct files, while the HTW and SIRET teams each had 1, and 2 wrongly identified files respectively, but in all 3 cases the correct file was later identified. The UU team achieved the best ratio for correct submissions vs. wrong submissions with 8 correct submissions to 3 wrong submissions.
In the case of the successful submissions (see Fig. 12a) a greater number of submissions have been issued with frames from the second half of the target segment (positive values), although there is also a significant number of submissions from the first half. In the case of the false submissions (Fig. 12b) most of them are made by indicating frames/segments past the target segment and later in the video.
The actual frames sent for validation by the participating teams, for all the successful submissions, can be seen in Table 6 as thumbnails with green contour (in the case of the IMOTION team which sent a frame range, as permitted by the competition rules, we have chosen the central frame of the sent segment).
6 Evaluation details on textual experts tasks
The final scores at the end of the text round are also shown in Fig. 2. This proved to be the most challenging round of the competition. From the nine participating teams, only the UU team managed to score more than 50 % of the possible points for the session, with 367 points out of 600, while VERGE and SIRET scored close to 33 % with 188 and 166 points respectively. The performance of the UU team is particularly surprising since it employed only human computation and only static small thumbnails (no audio or video playback capabilities). Those results show there is still enough room for improvement in this area.
Regarding task completion, no team managed to solve Task 4, while Task 1 and Task 6 were solved only by the UU team. Task 2 and Task 3 were successfully solved by 3 teams, while Task 5 seams to have been the easiest, with 5 teams solving it. All teams scored over 50 % of the available points per task (more than 50 points) and as in the case of the visual round, no successful submission was made past the 5 minutes mark.
7 Evaluation details on the novice tasks
Figure 2 also shows the scores obtained by each of the participating teams for the visual and text tasks of the novice round. With four visual tasks and two text tasks, the maximum possible scores were 400 for the visual and 200 for the text tasks. This gave an overall of 600 possible points for the novice round, as much as the text expert round.
IMOTION obtained the highest score for the visual novice (340 points, close to the maximum of 400), but the lowest score (28 points from the maximum of 200) for the text novice. NII-UIT, HTW, SIRET, VERGE and UU also scored high in the visual novice tasks. For the text novice tasks, NII-UIT, HTW and SIRET obtained the highest scores, with NII-UIT scoring a surprisingly high score of 181 points. The UU team also managed to score 90 points while, as already mentioned, IMOTION were last in this category with only 28 points. VERGE scored no points for the text tasks in the novice round, which is very surprising, since their concept-based search tool seems to be particularly well suited for novices.
it was the final round which was to decide the winner in a very tight competition and the participants were over-cautious;
the majority of the“novices” were in fact members of the participating teams testing the “competition’s” tools under their colleagues close supervision and they did not want to “sabotage” their winning chances by making wrong submissions and by this achieving a low score. At that point we want to mention that the novice session in general is kind of problematic for the final analysis, as it may distort the results. Therefore, we might want to skip it in future iterations of the VBS.
A closer inspection revealed that there is no difference between the two types of tasks (visual vs. text) from the outcome of submissions point of view. It can be seen though, that Task 6 seemed more difficult since it had overall the largest number of false submissions in both the right and wrong files. It has also been a textual task. The unusual large number of submissions for Task 6 when compared to the other five previous tasks could be also explained by the fact that it was the last task and by an all-or-nothing approach from some of the participants. In fact, the winner of the competition was decided by novices during this last task, when SIRET overcame IMOTION, after three false submissions for each team: two vs. one from a wrong file for SIRET when compared to IMOTION. The difference was made by the speed of the submissions, with SIRET being twice as fast as IMOTION for this particular task with 117049 ms vs. 268769 ms.
8 Development of interactive video search tools
VBS sessions happen to be indeed interactive and VBS 2015 was not an exception. Participants are exploring tools of other teams and the audience often discuss the approaches during breaks etc. It is thus natural to adapt and perhaps enhance a well-performing feature introduced by some other participant. Thanks to the gradual improvements, a tool winning the competition one year would probably fail without further development the next year. Several teams participated steadily for the last few years, each year improving their tools, adding modalities, features or ever reworking their concepts from scratch. We may ask then, are there any trends that we can distinguish? Can we find a common feature that is sooner or later incorporated by almost every team? And lastly, can we derive guides or best practices for developing such interactive video search tool?
In the following paragraphs, we track the evolution of the tools which had won VBS in one of the previous years, namely teams AAU (2012), NII-UIT (2013) and SIRET (2014 and 2015).
NII-UIT established themselves in 2013 by actually winning VBS . The tool utilized filtering by prior-detected concepts and visual content, a grid of dominant color more precisely. The results were presented with a coarse-to-fine hierarchical refinement approach. In 2014 they came up with quite a similar tool with one important enhancement – a user could define a sequence of patterns, i.e., define two sets of filters and search for clips having two matching scenes in the same order . Finally, in 2015 they additionally focused on face and upper body concepts together with audio filters and replaced the grid of dominant colors with less rigid free-drawing canvas .
The tool which was introduced by SIRET team in 2014  appeared quite different. Instead of complex processing pipelines, such as state-of-the-art concept detectors etc., the authors employed only one feature capturing color distribution of key-frames (so called feature signatures) together with convenient sketch drawings (apparently, NII-UIT adapted sketch drawings later on). Surprisingly it was enough to win the VBS that year. Note that similarly to NII-UIT’s tool, users were allowed to specify two consecutive sketches to improve the filtering power which seemed to be quite effective. Changes introduced to the tool in 2015  were rather subtle, focusing the browsing part of the tool, such as compacting static scenes in order to save space etc.
The list of winners of previous VBS competitions is completed by AAU tool from 2012  which is somewhat similar to the most recent UU’s application, both exhibiting surprisingly powerful human computation potential. In this case, the videos are simply scanned in parallel during the search time without any prior content analysis.
Content-based filtering may be based on either high-level concepts or low-level features. In particular, most of the participants used some kind of color-based filtering and although their filtering power decreases steadily as the dataset size increases, they seem to remain quite effective. Also, temporal filtering (e.g., two content sketches that focus two neighboring segments in a video) seem to be quite effective, because static approaches (focusing on a single image only) do not work well with the sheer amount of frames.
Browsing is perhaps a crucial part that cannot be avoided. In many cases, the number of relevant results is simply too large to fit one screen and users need an effective and convenient way to browse through the results.
As users do so, they will probably encounter scenes quite similar to the searched one which may be used as a query for additional similarity search. By giving the search engine either positive or negative examples users can rapidly navigate themselves towards the target if an appropriate similarity model is employed. Note that regarding the textual tasks, we face the problem of proper initialization of this similarity search loop.
At this moment, we do not see a single approach, feature or concept that is clearly outperforming the others. We believe, though, that a successful interactive video search tool has to incorporate all the three techniques mentioned above.
It is of course hard to predict the future in this challenging field. However, we can assume that future systems will also strongly rely on color-based filtering (e.g., color maps such as used by the system described in Section 4.3), on concept-based filtering (e.g., visual semantic concepts detected with deep learning approaches), on temporal filtering as well as on improved content visualization with several techniques, for example with hierarchical refinement of similar results. Since the VBS plans to increase the size of the data set every year, we believe that in the long run the biggest challenge will be the efficient handling of the large amount of content, i.e., content descriptors and indexes, and providing a highly responsive interactive system that allows for iterative refinement.
In the context of the discussion that follows, we would like to highlight the fact that all target segments for the three rounds, were randomly generated from the 154 files totaling over 100 hour of video material that formed the competition dataset. Approximately 10% of the cues had also textual description assigned by the organizers. From within those two pools, the target videos for the competition rounds were randomly chosen: ten targets for the visual expert round, six targets for the text expert round and six targets for the novice round (four targets for visual tasks and two targets for text tasks).
The case of the novice round differs a little bit from the two expert rounds, because the visual and textual tasks were mixed and not consecutive. Also, because of time constraints, the organizers were able to allow only six novice tasks out of which four were visual (Task 1, Task 2, Task 4 and Task 5) and two were textual tasks (Task 3 and Task 6).
The visual tasks in the novice round achieved the best overall scores across all teams when compared with the visual tasks in the visual expert round.
the text tasks in the novice round (2 tasks) achieved comparable results with the best performances across the 6 tasks in the text expert round.
The completion time in the case of the visual and text tasks in the novice round is comparable with the completion time in the case of the visual and text expert rounds. The main difference is that the advantage that the SIRET, IMOTION and sometimes UU teams had in the expert rounds, is much reduced in the case of the novice round.
The novice round brought the best performance for the NII-UIT team in both scored points and speed. Actually for the first text task in the novice round, the NII-UIT team achieved the best score and had the fastest correct submission.
Within each of the scenes used as targets in the visual expert round there are multiple highly similar images (this is also apparent in Table 1 as well as Tables 3 and 4 which display overviews of the 20 seconds long target scenes while using 2 second granularity for each image). Because of the granularity used in the figures, not all the details are visible, from here the difference in terms of actually submitted frames.
The scenes are very diverse including indoor and outdoor shots as well as overlays of computer generated content spread across TV reporting, TV series, TV documentaries.
The best results were obtained by tools that employed some form of sketching for an query-by-example approach, as in the case of the SIRET and IMOTION teams, or that made heavy use of browsing, like in the case of the UU team which had an approach centered on human computation. All those tools had effectively put the user in the center of their approaches to an interactive multimedia retrieval system and had tried to exploit its mental and physical capacities to their fullest in order to solve the proposed tasks. The results of the text tasks during both the expert and novice rounds show that there is still a lot of room for improvement and that in this particular case further research is needed.
Open access funding provided by University of Klagenfurt.
The authors would like to thank all the colleagues that participated in the development of the VBS2015 tools, the colleagues that took part in the VBS2015 competition as well as the colleagues that took time to read and make observations on draft versions of this paper: Laszlo Böszörmenyi, Marco A. Hudelist, Anastasia Moumtzidou , Vasileios Mezaris, Ioannis Kompatsiaris, Rob van de Werken, Nico Hezel, Radek Mackowiak, Ivan Giangreco, Claudiu Tănase, Heiko Schuldt.
The video content used for VBS is programme material under the copyright of the British Broadcasting Corporation (BBC), kindly provided for research use in the context of VBS.
This work was funded by the Federal Ministry for Transport, Innovation and Technology (bmvit) and Austrian Science Fund (FWF): TRP 273-N15 and the European Regional Development Fund and the Carinthian Economic Promotion Fund (KWF), supported by Lakeside Labs GmbH, Klagenfurt, Austria.
The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 610370, ICoSOLE (“Immersive Coverage of Spatially Outspread Live Events”, http://www.icosole.eu).
This research has been supported by Charles University Grant Agency project 1134316.
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