Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
FISH: a practical system for fast interactive image search in huge databases
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Efficient search with changing similarity measures on large multimedia datasets
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
IEEE Transactions on Image Processing
A memory learning framework for effective image retrieval
IEEE Transactions on Image Processing
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Content based image retrieval (CBIR) has been well studied in the computer vision and multimedia community. Content free image retrieval (CFIR) methods, and their complementary characteristics to CBIR has not received enough attention in the literature. Performance of CBIR is constrained by the semantic gap between the feature representations and user expectations, while CFIR suffers with sparse logs and cold starts. We fuse both of them in a Bayesian framework to design a hybrid image retrieval system by overcoming their shortcomings. We validate our ideas and report experimental results, both qualitatively and quantitatively. We use our indexing scheme to efficiently represent both features and logs, thereby enabling scalability to millions of images.