Improving image search effectiveness by integrating contextual information

Recently, image retrieval approaches shift to context-based reasoning. Context-based approaches proved their efficiency to improve retrieval process. In fact, conventional image search engines are often not able to satisfy the user's intent as they provide noisy or/and redundant results. In addition, when a query is ambiguous, such systems can hardly distinguish different meanings for one query and therefore, they fail to show images with different contexts. A good system should provide, at top-k results, images which are the most relevant and diverse to guarantee user's satisfaction. Our objective is to improve the retrieval process performance by harnessing the contextual information to measure the relevance score and diversity score. The proposed approach implies the relevance-based ranking where a random walk with restart offers a refining step, the diversity-based ranking and the combination. Our approach was evaluated in the context of ImageCLEF1 benchmark. Obtained results are promising especially for diversity-based ranking.

[1]  Chokri Ben Amar,et al.  Effective concept detection using Second order Co-occurence Flickr context similarity measure SOCFCS , 2012, 2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI).

[2]  Frank Nielsen,et al.  K-MLE: A fast algorithm for learning statistical mixture models , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Samy Bengio,et al.  Large Scale Online Learning of Image Similarity Through Ranking , 2009, J. Mach. Learn. Res..

[4]  Minglun Gong,et al.  Balancing the Trade-Offs Between Diversity and Precision for Web Image Search Using Concept-Based Query Expansion , 2012 .

[5]  Roberto Tronci,et al.  Image Hunter at ImageCLEF 2012 Personal Photo Retrieval Task , 2012, CLEF.

[6]  Xiangdong Zhou,et al.  Exploring Flickr's related tags for semantic annotation of web images , 2009, CIVR '09.

[7]  Been-Chian Chien,et al.  KIDS Lab at ImageCLEF 2012 Personal Photo Retrieval , 2012, CLEF.

[8]  Jiwu Huang,et al.  Salient covariance for near-duplicate image and video detection , 2011, 2011 18th IEEE International Conference on Image Processing.

[9]  Chokri Ben Amar,et al.  REGIMvid at ImageCLEF2012: Improving Diversity in Personal Photo Ranking Using Fuzzy Logic , 2012, CLEF.

[10]  Chokri Ben Amar,et al.  Flickr-based semantic context to refine automatic photo annotation , 2012, 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA).

[11]  Meng Wang,et al.  Social Image Search with Diverse Relevance Ranking , 2010, MMM.

[12]  Hermann Ney,et al.  Jointly optimising relevance and diversity in image retrieval , 2009, CIVR '09.

[13]  Chokri Ben Amar,et al.  REGIMvid at ImageCLEF2012: Concept-based Query Refinement and Relevance-based Ranking Enhancement for Image Retrieval , 2012, CLEF.

[14]  Martine De Cock,et al.  Diversification of search results as a fuzzy satisfiability problem , 2011, ECIR 2011.

[15]  Matthieu Cord,et al.  Efficient Bag-of-Feature kernel representation for image similarity search , 2011, 2011 18th IEEE International Conference on Image Processing.