Title :
Representing Meanings of Images Based on Associative Values with Lexicons
Author_Institution :
Fac. of Software & Inf. Sci., Iwate Pref Univ., Takizawa, Japan
Abstract :
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.
Keywords :
image representation; image retrieval; learning (artificial intelligence); BAM; SR factor; STRM; VS factor; associative value generation; bidirectional associative memory; image meaning representation; image retrieval; learning pattern image; lexicon; semantic relevance; semantic tolerance relation model; visual similarity; Associative memory; Database languages; Humans; Image analysis; Image color analysis; Image retrieval; Magnesium compounds; Signal processing; Strontium; Videos; associative values; image/videos; lexicon; sementic relevance; visual similarity;
Conference_Titel :
Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP '09. Fifth International Conference on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4244-4717-6
Electronic_ISBN :
978-0-7695-3762-7
DOI :
10.1109/IIH-MSP.2009.32
