Enhancing Visual Search by Using Image Re-ran
Abstract
The existing web image search engines, including Bing, Google, and Yahoo, retrieve and rank images mostly based on surrounding text features[8][12]. Image redundancy is still a problem area concerned. It is difficult for them to interpret users search intention only by query keywords and this leads to ambiguous and noisy search results which are far from satisfactory. It is important to use visual information in order to solve the ambiguity in text-based image retrieval. In this paper, we have proposed a novel image search approach. It only requires the user to click on one query image with minimum effort and images from a pool retrieved by textbased search are re-ranked based on both visual and textual content.
References
[9] A. W. M. Smeulders, M.Worring, S. Santini, and A. Gupta, Content based image retrieval at the end of the early years, IEEE Trans Pattern Anal. Mach Intel., vol. 22, no. 12, pp. 1349–1380, Dec. 2000. [10] F. Richter, S. Romberg, E. Horster, and R. Lienhart, “Multimodal ranking for image search on community databases,” in Proc. ACM SIGMM Int. Workshop Multimedia Inf. Retr. , 2010, pp. 63–72. [11] Tao Mai, Yong Rui, Shipeng Li, Microsoft Research Asia, Qi Tian, Multimedia Search Reranking: A literature Survey, ACM Journal, Vol. 2, No., 3, Article 1, May 2012. [12] Xiaopeng Yang, Yongdong Zhang, Ting Yao,Chong-Wah Ngo, Tao Mei; Click-boosting multi-modality graph-based reranking for image Search, Multimedia Systems, DOI 10.1007/s00530-014-0379-8, Springer- Verlag Berlin Heidelberg 2014, Published online 09 May 2014. [13] R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Addison-Wesley Longman Publishing Co., 1999. [14] Y. Chen, X. Zhou, and T. Huang, “One-Class SVM for Learning in Image Retrieval,” Proc. IEEE Int’l Conf. Image Processing, 2001.
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