This paper presents a new approach for building semantic image indexing and retrieval systems. Our approach is composed of four phases : (1) knowledge acquisition, (2) weakly-super...
We present a class of models that are discriminatively trained to directly map from the word content in a query-document or documentdocument pair to a ranking score. Like Latent Se...
Bing Bai, Jason Weston, David Grangier, Ronan Coll...
High-dimensional index is one of the most challenging tasks for content-based video retrieval (CBVR). Typically, in video database, there exist two kinds of clues for query: visual...
Zhiping Shi, Qingyong Li, Zhiwei Shi, Zhongzhi Shi
Latent Semantic Indexing (LSI) has been shown to be effective in recovering from synonymy and polysemy in text retrieval applications. However, since LSI ignores class labels of t...
Sutanu Chakraborti, Rahman Mukras, Robert Lothian,...
To bridge the semantic gap in content-based image retrieval, detecting meaningful visual entities (e.g. faces, sky, foliage, buildings etc) in image content and classifying images...