Abstract
We propose a novel multimodal approach to automatically predict the visual concepts of images through an effective fusion of visual and textual features. It relies on a Selective Weighted Late Fusion (SWLF) scheme which, in optimizing an overall Mean interpolated Average Precision (MiAP), learns to automatically select and weight the best features for each visual concept to be recognized. Experiments were conducted on the MIR Flickr image collection within the ImageCLEF Photo Annotation challenge. The results have brought to the fore the effectiveness of SWLF as it achieved a MiAP of 43.69 % in 2011 which ranked second out of the 79 submitted runs, and a MiAP of 43.67 % that ranked first out of the 80 submitted runs in 2012.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Salton G, McGill MJ (1986) Introduction to modern information retrieval. McGraw-Hill Inc, New York
Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22:1349–1380
Picard RW (2000) Affective computing. MIT press, Cambridge
Mojsilović A, Gomes J, Rogowitz B (2004) Semantic-friendly indexing and quering of images based on the extraction of the objective semantic cues. Int J Comput Vision 56:79–107
Snelick R, Uludag U, Mink A, Indovina M, Jain A (2005) Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE Trans Pattern Anal Mach Intell 27:450–455
Machajdik J, Hanbury A (2010) Affective image classification using features inspired by psychology and art theory. In: Proceedings of the international conference on Multimedia, ACM, pp 83–92
Liu N, Dellandréa E, Chen L, Zhu C, Zhang Y, Bichot CE, Bres S, Tellez B (2013) Multimodal recognition of visual concepts using histograms of textual concepts and selective weighted late fusion scheme. Comput Vis Image Underst 117:493–512
Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88:303–338
Smeaton AF, Over P, Kraaij W (2006) Evaluation campaigns and trecvid. In: MIR ’06: Proceedings of the 8th ACM international workshop on multimedia, information retrieval, pp 321–330
Nowak S, Nagel K, Liebetrau J (2011) The clef 2011 photo annotation and concept-based retrieval tasks. In: CLEF workshop notebook paper
Guillaumin M, Verbeek JJ, Schmid C (2010) Multimodal semi-supervised learning for image classification. In: Proceedings of CVPR, pp 902–909
Snoek CGM, Worring M, Smeulders AWM (2005) Early versus late fusion in semantic video analysis. In: Proceedings of the 13th annual ACM international conference on multimedia, pp 399–402
Ah-Pine J, Bressan M, Clinchant S, Csurka G, Hoppenot Y, Renders JM (2009) Crossing textual and visual content in different application scenarios. Multimedia Tools Appl 42:31–56
Snoek CGM, Worring M, Geusebroek JM, Koelma DC, Seinstra FJ (2004) The mediamill trecvid 2004 semantic video search engine. In: Proceedings of the TRECVID workshop
Westerveld T, Vries APD, van Ballegooij A, de Jong F, Hiemstra D (2003) A probabilistic multimedia retrieval model and its evaluation. EURASIP J Appl Signal Process 2003:186–198
Noble WS et al (2004) Support vector machine applications in computational biology. In: Schoelkopf B, Tsuda K, Vert, J-P (eds) Kernel methods in computational biology. MIT Press, Cambridge, pp 71–92
Pinquier J, Karaman S, Letoupin L, Guyot P, Mégret R., Benois-Pineau J, Gaestel Y, Dartigues JF (2012) Strategies for multiple feature fusion with hierarchical hmm: application to activity recognition from wearable audiovisual sensors. In: Proceedings of 21st international conference on pattern recognition (ICPR), IEEE, pp 3192–3195
Binder A, Samek W, Kloft M, Müller C, Müller KR., Kawanabe M (2011) The joint submission of the tu berlin and fraunhofer first (tubfi) to the imageclef2011 photo annotation task. In: CLEF workshop notebook paper
Nagel K, Nowak S, Kühhirt U, Wolter K (2011) The Fraunhofer IDMT at ImageCLEF 2011 photo annotation task. In: Proceedings of CLEF (Notebook Papers/Labs/Workshop)
Csurka G, Dance C, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, ECCV, vol 1, p 22
Quenot G, Benois-Pineau J, Mansencal B, Rossi E, Cord M, Precioso F, Gorisse D, Lambert P, Augereau B, Granjon L et al (2008) Rushes summarization by IRIM consortium: redundancy removal and multi-feature fusion. In: Proceedings of the 2nd ACM TRECVid video summarization workshop, ACM, pp 80–84
Wu Y, Chang EY, Chang KCC, Smith JR (2004) Optimal multimodal fusion for multimedia data analysis. In: Proceedings of the 12th annual ACM international conference on Multimedia, pp 572–579
Znaidia A, Borgne HL, Popescu A (2011) CEA list’s participation to visual concept detection task of ImageCLEF 2011. In: CLEF workshop notebook paper
Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20:226–239
Ben Soltana W, Huang D, Ardabilian M, Chen L, Ben Amar C (2010) Comparison of 2D/3D features and their adaptive score level fusion for 3D face recognition. In: 3D data processing, visualization and transmission (3DPVT)
Pudil P, Novovičová J, Kittler J (1994) Floating search methods in feature selection. Pattern Recogn Lett 15:1119–1125
Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33:1–39
Breiman L, Breiman L (1996) Bagging predictors. Mach Learn 24:123–140
Fergus R, Fei-Fei L, Perona P, Zisserman A (2005) Learning object categories from google’s image search. In: 10th IEEE international conference on computer vision ICCV, IEEE, vol 2, pp 1816–1823
Schroff F, Criminisi A, Zisserman A (2007) Harvesting image databases from the web. In: IEEE 11th international conference on computer vision, ICCV, pp 1–8
Wang G, Hoiem D, Forsyth DA (2009) Building text features for object image classification. In: Proceedings of CVPR, pp 1367–1374
Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29:51–59
Zhu C, Bichot CE, Chen L (2010) Multi-scale color local binary patterns for visual object classes recognition. In: Proceedings of ICPR, pp 3065–3068
Pujol A, Chen L (2007) Line segment based edge feature using hough transform. In: Proceedings of the 7th IASTED international conference on visualization, imaging and image processing, ACTA Press, pp 201–206
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110
Mikolajczyk K, Schmid C (2004) Scale and affine invariant interest point detectors. Int J Comput Vis 60:63–86
Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of CVPR, vol 2, pp 2169–2178
Li FF, Perona P (2005) A Bayesian hierarchical model for learning natural scene categories. In: Proceedings of CVPR, vol 2, pp 524–531
Van de Sande KEA, Gevers T, Snoek CGM (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell 32:1582–1596
Tola E, Lepetit V, Fua P (2010) Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans Pattern Anal Mach Intell 32:815–830
Zhu C, Bichot CE, Chen L (2011) Visual object recognition using daisy descriptor. In: Proceedings of ICME, pp 1–6
Dunker P, Nowak S, Begau A, Lanz C (2008) Content-based mood classification for photos and music: a generic multi-modal classification framework and evaluation approach. In: Proceedings of multimedia information retrieval, pp 97–104
Liu N, Dellandréa E, Tellez B, Chen L (2011) Evaluation of features and combination approaches for the classification of emotional semantics in images. In: International conference on computer vision, theory and applications (VISAPP)
Liu N, Dellandréa E, Tellez B, Chen L, Chen L (2011) Associating textual features with visual ones to improve affective image classification. In: Proceedings of ACII, vol 1, pp 195–204
Valdez P, Mehrabian A (1994) Effects of color on emotions. J Exp Psychol Gen 123:394–409
Itten J, Van Haagen E (1973) The art of color: the subjective experience and objective rationale of color. Van Nostrand Reinhold, New York
Tamura H, Mori S, Yamawaki T (1978) Texture features corresponding to visual perception. IEEE Trans Syst Man Cybern 8(6):460–472
Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67:786–804
Anstey NA (1966) Correlation techniques—a reivew. Can J Explor Geophys 2:55–82
van de Sande K. Colordescriptor software. http://www.colordescriptors.com
Colombo C, Bimbo AD, Pala P (1999) Semantics in visual information retrieval. IEEE Multimedia 6:38–53
Dellandréa E, Liu N, Chen L (2010) Classification of affective semantics in images based on discrete and dimensional models of emotions. In: International workshop on content-based multimedia indexing (CBMI), pp 99–104
Ke Y, Tang X, Jing F (2006) The design of high-level features for photo quality assessment. In: IEEE computer society conference on computer vision and pattern recognition, vol 1, pp 419–426
Datta R, Li J, Wang JZ (2005) Content-based image retrieval: approaches and trends of the new age. In: Proceedings on multimedia information retrieval, pp 253–262
Viola PA, Jones MJ (2001) Robust real-time face detection. In: Proceedings of CCV, vol 57, pp 137–154
Budanitsky A, Hirst G (2001) Semantic distance in wordnet: an experimental, application-oriented evaluation of five measures. In: Workshop on WordNet and other lexical resources, 2nd meeting of the North American chapter of the association for computational linguistics
Miller GA (1995) Wordnet: a lexical database for English. Commun ACM 38:39–41
Huiskes MJ, Lew MS (2008) The MIR flickr retrieval evaluation. In: Proceedings on multimedia information retrieval, pp 39–43
Huiskes MJ, Thomee B, Lew MS (2010) New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative. In: MIR ’10: Proceedings of the 2010 ACM international conference on multimedia, information retrieval, pp 527–536
Vapnik VN (1995) The nature of statistical learning theory. Springer New York Inc., New York
Zhang J, Marszaek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: a comprehensive study. Int J Comput Vis 73:213–238
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27
Escalante HJ, Montes M, Sucar E (2011) Multimodal indexing based on semantic cohesion for image retrieval. Inf Retrieval 15:1–32
van Gemert JC, Veenman CJ, Smeulders AWM, Geusebroek JM (2010) Visual word ambiguity. IEEE Trans Pattern Anal Mach Intell 32:1271–1283
Liu N, Zhang Y, Dellandréa E, Bres S, Chen L (2012) LIRIS-Imagine at ImageCLEF 2012 photo annotation task. In: CLEF workshop notebook paper
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Liu, N., Dellandréa, E., Tellez, B., Chen, L. (2014). A Selective Weighted Late Fusion for Visual Concept Recognition. In: Ionescu, B., Benois-Pineau, J., Piatrik, T., Quénot, G. (eds) Fusion in Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-05696-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-05696-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05695-1
Online ISBN: 978-3-319-05696-8
eBook Packages: Computer ScienceComputer Science (R0)