From Features to Semantics: Some Preliminary Results

In this paper, we present the results of a project that seeks to transform low-level features to a higher level of meaning. This project concerns a technique, latent semantic analysis (LSA), which has been used for full-text retrieval for many years. In this environment, LSA determines clusters of co-occurring keywords, sometimes, called concepts, so that a query which uses a particular keyword can then retrieve documents perhaps not containing this keyword, but containing other keywords from the same cluster. In this paper, we examine the use of this technique for content-based image retrieval, using two different approaches to image feature representation.

• Type: Conference
• Year: 2000
• Where: ICMCS
• Authors: Rong Zhao, William I. Grosky
• Keywords: Content-based Image Retrieval, ICMCS 2000, Latent Semantic Analysis, LSA Determines Clusters, Multimedia