Abstract
Existing Web image search engines index images by textual descriptions including filename, image caption, surrounding text, etc. However, the textual description available on the Web could be ambiguous or inaccurate in describing the actual image content and some images irrelevant to user’s query are also returned by text-based search engines. In this paper, we propose to integrate the existing text-based image search engine with visual features, in order to improve the performance of pure text-based Web image search. The proposed algorithm is named SIEVE. Practical fusion methods are proposed to integrate SIEVE with contemporary text-based search engines. In our approach, text-based image search results for a given query are obtained first. Then, SIEVE is used to filter out those images which are semantically irrelevant to the query. Experimental results show that the image retrieval performance using SIEVE improves over Google image search significantly.
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References
Thao, C., Munson, E.V.: A Relevance Model for Web Image Search. In: Proc. of International Workshop on Web Document Analysis (WDA 2003) UK, August 3, 2003, pp. 57–60 (2003)
Lin, W.-H., Jin, R., Hauptmann, A.: Web Image Retrieval Re-Ranking with Relevance Model. In: Proc. of IEEE/WIC International Conference on Web Intelligence (WI’03), pp. 242–248 (2003)
Google image search: http://images.google.com (accessed in December 2006)
Yahoo image search: http://images.yahoo.com (accessed in December 2006)
Cai, D., He, X., Li, Z., Ma, W.-Y., Wen, J.-R.: Hierarchical Clustering of WWW Image Search Results using Visual, Textual and Link Information. In: Proc. of ACM Inter. Conf. on Multimedia, pp. 952–959 (2004)
He, J., Zhang, C., Zhao, N., Tong, H.: Boosting Web Image Search by Co-Ranking. In: Proc. of Int. Conf. on Acoustics, Speech, and Signal Processing, pp. 409–412 (2005)
Liu, Y., Zhang, D.S., Lu, G., Ma, W.-Y.: Region-Based Image Retrieval with High-Level Semantic Color Names. In: Proc. of IEEE 11th International Multi-Media Modelling Conference (MMM05), January 12-14, 2005, Melbourne, Australia, pp. 180–187 (2005)
Liu, Y., Zhang, D.S., Lu, G., Ma, W.-Y.: Region-based Image Retrieval with Perceptual Colors. In: Proc. of Pacific-Rim Multimedia Conference (PCM), December 2004, pp. 931–938 (2004)
Liu, Y., Zhang, D.S., Lu, G., Ma, W.-Y.: Study on Texture Feature Extraction in Region-Based Image Retrieval System. In: Proc. of Inter. Multimedia Modelling Conf (MMM 2006), Beijing, pp. 264–271 (2006)
Liu, Y., Ma, W., Zhang, D.S., Lu, G.: An Efficient Texture Feature Extraction Algorithm for Arbitrary-Shaped Regions. In: Proc. of IEEE 7th International Conference on Signal Processing (ICSP2004), Beijing, China August 31-September 4, 2004, vol. 2, pp. 1037–1040 (2004)
Liu, Y., Zhang, D.S., Lu, G.: Deriving High-Level Concepts Using Fuzzy-ID3 Decision Tree for Image Retrieval. In: Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP05), March 18-23, 2005, pp. 501–504. Philadelphia, PA, USA (2005)
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© 2007 Springer Berlin Heidelberg
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Liu, Y., Zhang, D., Lu, G. (2007). SIEVE—Search Images Effectively Through Visual Elimination. In: Sebe, N., Liu, Y., Zhuang, Y., Huang, T.S. (eds) Multimedia Content Analysis and Mining. MCAM 2007. Lecture Notes in Computer Science, vol 4577. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73417-8_46
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DOI: https://doi.org/10.1007/978-3-540-73417-8_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73416-1
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