Relevance feedback for semantic classification: A comparative study

Abstract

Immense increase in the number of multimedia content accessible from television and internet with the help developing technologies reveals efficient supervision and classification of such content as a problem. Relevance feedback is a technique which relies on evaluation of retrieval results by humans and enables reduce the semantic gap between ideas and low level representations. Content based high level classification system may employ relevance feedback for improved retrieval performance. In this paper, different relevance feedback algorithms, which can be utilized to increase generalized semantic classification performance, are discussed and compared inside an experimental framework. Some improvements are also proposed over obtained results.