Title :
A Neural Network Approach for Bridging the Semantic Gap in Texture Image Retrieval
Author :
Li, Qingyong ; Shi, Zhiping ; Luo, Siwei
Author_Institution :
Beijing Jiaotong Univ., Beijing
Abstract :
One of the big challenges faced by content-based image retrieval (CBIR) is the ´semantic gap´ between the visual features and the richness of human semantics for image content. We put forward a neural network approach to extract the image fuzzy semantics ground on linguistic expression based image description framework (LEBID). We utilize the linguistic variable to depict the texture semantics according to Tamura texture model, so we can describe the image in linguistic expression such as coarse, very line-like. Moreover, we use feedforward neural network (NN) to model the vagueness of human visual perception and to extract the fuzzy semantic feature. Our experiments demonstrate that NN outperforms other method such as genetic algorithm on the complexity of model, and it also achieves good retrieval performance.
Keywords :
content-based retrieval; feedforward neural nets; fuzzy set theory; image retrieval; image texture; Tamura texture model; content-based image retrieval; feedforward neural network; fuzzy semantic feature; human semantics; human visual perception; image content; image description framework; image fuzzy semantics; linguistic expression; semantic gap; texture image retrieval; texture semantics; visual features; Content based retrieval; Face; Fuzzy neural networks; Genetic programming; Humans; Image retrieval; Information retrieval; Information technology; Machine learning; Neural networks;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371021