KTW 2011: Knowledge Technology pp 262-271 | Cite as
High Level Semantic Concept Retrieval Using a Hybrid Similarity Method
Abstract
In video search and retrieval, user’s need is expressed in terms of query. Early video retrieval systems usually matched video clips with such low-level features as color, shape, texture, and motion. In spite of the fact that retrieval is done accurately and automatically with such low-level features, the semantic meaning of the query cannot be expressed in this way. Moreover, the limitation of retrieval using desirable concept detectors is providing annotations for each concept. However, providing annotation for every concept in real world is very challenging and time consuming, and it is not possible to provide annotation for every concept in the real world. In this study, in order to improve the effectiveness of the retrieval, a method for similarity computation is proposed and experimented for mapping concepts whose annotations are not available onto the annotated and known concepts. The TRECVID 2005 dataset is used to evaluate the effectiveness of the concept-based video retrieval model by applying the proposed similarity method. Results are also compared with previous similarity measures used in the same domain. The proposed similarity measure approach outperforms other methods with the Mean Average Precision (MAP) of 26.84% in concept retrieval.
Keywords
Video Retrieval Video Analysis Semantic Knowledge Similarity MeasuresPreview
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References
- 1.Allison, L., Dix, T.I.: A bit-string longest-common-subsequence algorithm. Inf. Process. Lett. 23(6), 305–310 (1986)MathSciNetCrossRefGoogle Scholar
- 2.Awang Iskandar, D.N.F., Thom, J.A., Tahaghoghi, S.M.M.: Content-based image retrieval using image regions as query examples. In: Proceedings of the Nineteenth Conference on Australasian Database, ADC 2008, vol. 75, pp. 38–46. Australian Computer Society, Inc., Darlinghurst (2007), http://portal.acm.org/citation.cfm?id=1378307.1378319
- 3.Aytar, Y., Shah, M., Luo, J.: Utilizing semantic word similarity measures for video retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska (2008)Google Scholar
- 4.Bergroth, L., Hakonen, H., Raita, T.: A survey of longest common subsequence algorithms. In: Proceedings of the Seventh International Symposium on String Processing Information Retrieval (SPIRE 2000), p. 39. IEEE Computer Society (2000)Google Scholar
- 5.Berry, M.W.: Large scale sparse singular value computations. International Journal of Supercomputer Applications 6, 13–49 (1992)Google Scholar
- 6.Foltz, P.W., Kintsch, W., Landauer, T.K.: The measurement of textual coherence with latent semantic analysis (1998)Google Scholar
- 7.Haubold, A., Natsev, A.: Proceedings of the 2008 International Conference on Content-based Image and Video Retrieval, pp. 437–446. ACM, Niagara Falls (2008)CrossRefGoogle Scholar
- 8.Hauptmann, A., Yan, R., Lin, W., Christel, M., Wactlar, H.: Can High-Level concepts fill the semantic gap in video retrieval? a case study with broadcast news. IEEE Transactions on Multimedia 9(5), 958–966 (2007)CrossRefGoogle Scholar
- 9.Islam, A., Inkpen, D.: Semantic text similarity using corpus-based word similarity and string similarity. ACM Trans. Knowl. Discov. Data 2(2), 1–25 (2008)CrossRefGoogle Scholar
- 10.Jiang, Y.G., Ngo, C.W., Chang, S.F.: Semantic context transfer across heterogeneous sources for domain adaptive video search. In: Proceedings of the Seventeen ACM International Conference on Multimedia, MM 2009, pp. 155–164. ACM, New York (2009)CrossRefGoogle Scholar
- 11.Jiang, Y., Ngo, C., Yang, J.: Towards optimal bag-of-features for object categorization and semantic video retrieval. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 494–501. ACM, Amsterdam (2007)Google Scholar
- 12.Kondrak, G.: N-Gram Similarity and Distance. In: Consens, M.P., Navarro, G. (eds.) SPIRE 2005. LNCS, vol. 3772, pp. 115–126. Springer, Heidelberg (2005)CrossRefGoogle Scholar
- 13.Landauer, T., Foltz, P., Laham, D.: An introduction to latent semantic analysis. Discourse Processes (25), 259–284 (1998)Google Scholar
- 14.Melamed, I.D.: Bitext maps and alignment via pattern recognition. Comput. Linguist. 25(1), 107–130 (1999)Google Scholar
- 15.Mihalcea, R., Corley, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: AAAI 2006, pp. 775—780 (2006)Google Scholar
- 16.Naphade, M., Smith, J.R., Tesic, J., Chang, S., Hsu, W., Kennedy, L., Hauptmann, A., Curtis, J.: Large-Scale concept ontology for multimedia. IEEE Multimedia 13(3), 86–91 (2006)CrossRefGoogle Scholar
- 17.Snoek, C., Koelma, D., van Rest, J., Schipper, N., Seinstra, F., Thean, A., Worring, M.: Mediamill: Searching multimedia archives based on learned semantics. In: IEEE International Conference on Multimedia and Expo, ICME 2005, pp. 1575–1577 (2005)Google Scholar
- 18.Turney, P.: Mining the web for synonyms: PMI-IR versus LSA on TOEFL (2001)Google Scholar
- 19.Wolfe, M.B.W., Goldman, S.R.: Use of latent semantic analysis for predicting psychological phenomena: two issues and proposed solutions. Behavior Research Methods, Instruments, & Computers: A Journal of the Psychonomic Society, Inc. 35(1), 22–31 (2003); PMID: 12723777Google Scholar
- 20.Yanagawa, A., Chang, S.F., Kennedy, L., Hsu, W.: Columbia university’s baseline detectors for 374 lscom semantic visual concepts. Columbia University ADVENT technical report (2007)Google Scholar