A neural network approach for bridging the semantic gap in texture image retrieval

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.