This paperproposes a novel view of the information generated by relevance feedback. Latent semantic analysis is adapted to this view to extract useful inter-query information. The view presented in this paper is that thefundamental vocabulary of the system is the images in the database and that relevance feedback is a document whose words are the images. A relevance feedback document contains the intra-query information which expresses the semantic intent of the user over that query. The inter-query information then takes the form of a collection of documents which can be subjected to latent semantic analysis. An algorithm to query the latent semantic index is presented and evaluated against real data sets.
Inter-query learning is used in REF to improve the accuracy of a retrieval system with latent semantic analysis.
Building a latent semantic index of an image database from patterns of relevance feedback
Object recognition supported by user interaction for service robots
Object recognition supported by user interaction for service robots
Computer Science