Efficient Selection of Visual Features in Automatic Image Retrieval

Abstract

Feature selection is a process of finding an optimal subset of features from the original features set. It could solve the problem of the dimension disaster caused by high-dimensional features, which seriously affects the efficiency of the content-based image retrieval. This paper presents a method for generating an efficient feature reduction method of visual features with neighborhood rough set. By introducing the upper and lower approximation definition of neighborhood rough set, we calculate the approximation information to measure the relevance of the visual features. When the attributes of visual features are reduced and the rest of features can correctly describe the context of the images, the efficiency of the image retrieval could be improved. Furthermore, we use the selected the efficient visual features to perform the image retrieval. Experiment results show that the proposed algorithm is effective in comparison with the other mentioned methods.