A novel mapping method for image semantics and visual features

Abstract

Establishing a mapping relationship between image visual features and semantics can be used to reduce "semantic gap". A novel mapping method for image semantics and visual features was presented. In this method, image semantic information could be captured by adding users' relevance feedback, and then a decision table of visual features and semantics was constructed. Knowledge reduction of rough set theory was used to reduce the redundant visual features according to semantics, by doing that a mapping relationship between image visual features and its semantics was established. The experimental results indicate that amount of visual features irrelevant to image semantics can be reduced greatly, the complexity and cost of semantic classification are reduced and the accuracy of classification is better.