Effective invariant features for shape-based image retrieval

Abstract

The success of content-based image retrieval (CBIR) relies critically on the ability to find effective image features to represent the database images. The shape of an object is a fundamental image feature and belongs to one of the most important image features used in CBIR. In this article we propose a robust and effective shape feature known as the compound image descriptor (CID), which combines the Fourier transform (FT) magnitude and phase coefficients with the global features. The underlying FT coefficients have been shown analytically to be invariant to rotation, translation, and scaling. We also present details of the underlying innovative shape feature extraction method. The global features, besides being incorporated with the FT coefficients to form the CID, are also used to filter out the highly dissimilar images during the image retrieval process. Thus, they serve a dual purpose of improving the accuracy and hence the robustness of the shape descriptor, and of speeding up the retrieval process, leading to a reduced query response time. Experiment results show that the proposed shape descriptor is, in general, robust to changes caused by image shape rotation, translation, and/or scaling. It also outperforms other recently published proposals, such as the generic Fourier descriptor (Zhang & Lu, 2002).