Shape-Based Image Retrieval Using Combining Global and Local Shape Features

Content-based image retrieval (CBIR) has been an active research topic in the last decade. Using just one kind of feature information may cause inaccuracy compared with using more than two kinds of feature information. Aiming at shape-based image retrieval, in this paper we proposed an image retrieval method using the global and local shape features. Firstly, an image is segmented, and then the compactness and Fourier descriptor as local features are extracted. In order to remedy the effect of image segmentation on feature description and improve retrieval performance, global feature is extracted by Krawtchouk moment invariants. Finally, this approach uses the combined local and global shape features as feature vectors to achieve image retrieval. Experiments have been conducted on a database consisting of 500 images, compared with the method of using local shape features, experiments results show that this approach is more effective in image retrieval and improves the accuracy.