Reinier H. van Leuken
ABSTRACT
Due to the reliance on the textual information associated with an image, image search engines on the Web lack the discriminative power to deliver visually diverse search results. The textual descriptions are key to retrieve relevant results for a given user query, but at the same time provide little information about the rich image content.
In this paper we investigate three methods for visual diversification of image search results. The methods deploy lightweight clustering techniques in combination with a dynamic weighting function of the visual features, to best capture the discriminative aspects of the resulting set of images that is retrieved. A representative image is selected from each cluster, which together form a diverse result set.
Based on a performance evaluation we find that the outcome of the methods closely resembles human perception of diversity, which was established in an extensive clustering experiment carried out by human assessors.
- A. Bhattacharyya. On a measure of divergence between two statistical populations defined by probability distribution. Bul l. Calcutta Math. Soc., 35:99--109, 1493.Google Scholar
- D. Cai, X. He, Z. Li, W.-Y. Ma, and J.-R. Wen. Hierarchical clustering of www image search results using visual, textual and link information. In MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia, pages 952--959, New York, NY, USA, 2004. ACM. Google ScholarDigital Library
- S. A. Chatzichristo's and Y. S. Boutalis. Cedd: Color and edge directivity descriptor: A compact descriptor for image indexing and retrieval. In A. Gasteratos, M. Vincze, and J. K. Tsotsos, editors, ICVS, volume 5008 of Lecture Notes in Computer Science, pages 312--322. Springer, 2008. Google ScholarDigital Library
- R. Datta, D. Joshi, J. Li, and J. Z. Wang. Image retrieval: Ideas, influences, and trends of the new age. ACM Computer Surveys, 40(2):1--60, 2008. Google ScholarDigital Library
- E. Fowlkes and C. Mallows. A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78(383):553--569, 1983.Google ScholarCross Ref
- J. Goldberger, S. Gordon, and H. Greenspan. Unsupervised image-set clustering using an information theoretic framework. IEEE Transactions on Image Processing, 15(2):449--458, 2006. Google ScholarDigital Library
- J. Huang, S. R. Kumar, and R. Zabih. An automatic hierarchical image classification scheme. In MULTIMEDIA '98: Proceedings of the sixth ACM international conference on Multimedia, pages 219--228, 1998. Google ScholarDigital Library
- Y. Liu, D. Zhang, G. Lu, and W.--Y. Ma. A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 40(1):262--282, 2007. Google ScholarDigital Library
- M. MeilØa. Comparing clusterings : an information based distance. Journal of Multivariate Analysis, 98(5):873--895, 2007. Google ScholarDigital Library
- P.-A. Moellic, J.-E. Haugeard, and G. Pitel. Image clustering based on a shared nearest neighbors approach for tagged collections. In CIVR '08: Proceedings of the 2008 international conference on Content-based image and video retrieval, pages 269--278, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- P. Salembier and T. Sikora. Introduction to MPEG-7: Multimedia Content Description Interface. John Wiley & Sons, Inc., New York, NY, USA, 2002. Google ScholarDigital Library
- A. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intel ligence, 22(12):1349--1380, Dec 2000. Google ScholarDigital Library
- K. Song, Y. Tian, W. Gao, and T. Huang. Diversifying the image retrieval results. In MULTIMEDIA '06: Proceedings of the 14th annual ACM international conference on Multimedia, pages 707--710, New York, NY, USA, 2006. ACM. Google ScholarDigital Library
- H. Tamura, S. Mori, and T. Yamawaki. Texture features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics, 8(6), 1978.Google ScholarCross Ref
- R. van Zwol. Multimedia strategies for b3-sdr, based on principle component analysis. In Advances in XML Information Retrieval, Lecture Notes in Computer Science. Springer, 2006. Google ScholarDigital Library
- R. van Zwol, V. Murdock, L. Garcia, and G. Ramirez. Diversifying image search with user generated content. In Proceedings of the International ACM Conference on Multimedia Information Retrieval, 2008. Google ScholarDigital Library
- D. Wallace. Comment. Journal of the American Statistical Association, 78(383):569--576, 1983.Google Scholar
- S. Wang, F. Jing, J. He, Q. Du, and L. Zhang. Igroup: presenting web image search results in semantic clusters. In CHI '07: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 587--596, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- K. Weinberger, M. Slaney, and R. van Zwol. Resolving tag ambiguity. In Proceedings of the 16th International ACM Conference on Multimedia (MM 2008), Vancouver, Canada, November 2008. Google ScholarDigital Library
- P. Wilkins, P. Ferguson, and A. F. Smeaton. Using score distributions for query-time fusion in multimediaretrieval. In MIR '06: Proceedings of Google ScholarDigital Library
- the 8th ACM international workshop on Multimedia information retrieval, pages 51--60, New York, NY, USA, 2006. ACM.Google Scholar
- S. A. Yahia, P. Bhat, J. Shanmugasundaram, U. Srivastava, and E. Vee. Efficient online computation of diverse query results. In Proceedings of VLDB, 2007.Google Scholar
- B. Zhang, H. Li, Y. Liu, L. Ji, W. Xi, W. Fan, Z. Chen, and W.-Y. Ma. Improving web search results using affinity graph. In SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research Google ScholarDigital Library
- and development in information retrieval, pages 504--511, New York, NY, USA, 2005. ACM.Google Scholar
- C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In WWW '05: Proceedings of the 14th international conference on World Wide Web, pages 22--32, New York, NY, USA, 2005. ACM. Google ScholarDigital Library
Index Terms
- Visual diversification of image search results
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