Self-Growing RBF Neural Network Approach for Semantic Image Retrieval

Traditional methods of content-based image retrieval deal with the retrieval of images according to the similarity between them and the sample image in some low-level feature space such as color, shape and structure. But the relevant images satisfying user information need tend to have different distribution in the low-level feature space. In this case, the query image needs to be represented as multiple query images corresponding to the scattered relevant images. This paper proposes a new relevance feedback technique for semantic image retrieval which is based on the self-growing radial basis function (SGRBF) neural network. The approach can adaptively construct SGRBF neural network based on the users’ feedbacks. Thus, hidden nodes of the SGRBF neural network can represent the distribution of the users’ perceptual in the low-level feature space and bridge the semantic gap between low-level feature and high-level concept of the image content. The method is verified on a database of 1000 images and experimental results demonstrate that our method proposed in this paper is an effective method to promote semantic image retrieval performance.