Representing Meanings of Images Based on Associative Values with Lexicons

In this paper, an approach of representing meanings of images based on associative values with lexicons is proposed. For this, the semantic tolerance relation model (STRM) that reflects the tolerance degree between defined lexicons is generated, and two factors of semantic relevance (SR) and visual similarity (VS) are considered in generating associative values. Furthermore, the algorithm of calculating associative values using bidirectional associative memories (BAM), which is easy to implement, is depicted. The experiment results show that our proposed method improves the accuracy of retrieving images because of the introduction of SR and VS in representing meanings of images.