Image retrieval: content versus context
Thijs Westerveld · University of Twente Research Information · 2000
In this paper, we introduce a new approach to image retrieval. This new approach takes the best from two worlds, combines image features (content) and words from collateral text (context) into one semantic space. Our approach uses Latent Semantic Indexing, a method that uses co-occurrence statistics to uncover hidden semantics. This paper shows how this method, that has proven successful in both monolingual and cross lingual text retrieval, can be used for multi-modal and cross-modal information retrieval. Experiments with an on-line newspaper archive show that Latent Semantic Indexing can outperform both content based and context based approaches and that it is a promising approach for indexing visual and multi-modal data. 1 Introduction In the last few years, several research groups have been investigating content based image retrieval (Flickner et al., 1997; Gevers and Smeulders, 1997; Marsicoi, Clinque and Levialdi, 1997). A popular approach is querying by example and computing re...