Building a Latent Semantic Index of an Image Database from Patterns of Relevance Feedback

This paper proposes 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 the fundamental 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.

• Type: Conference
• Year: 2002
• Where: ICPR
• Authors: Douglas R. Heisterkamp
• Keywords: Computer Vision, ICPR 2002, Latent Semantic Analysis, Latent Semantic Index, Relevance Feedback Document