A modified Zernike moment shape descriptor invariant to translation, rotation and scale for similarity-based image retrieval

Abstract

Zernike moments are used as a shape descriptor for complex shapes such as trademarks that are difficult to be defined with a single contour for similarity-based image retrieval applications. Zernike moments of a given shape are calculated as correlation values of the shape with Zernike basis functions in that all the pixels of the shape regardless of their positions contribute with the same weight to the Zernike moments. The proposed modified Zernike moment descriptor for a shape is obtained taking account of the importance of the outer form of the shape to human perception. The modified Zernike moment descriptor is obtained by first dividing the original shape into two parts of inner and outer regions with a predetermined radius and then calculating the Zernike moment of the outer part and the inner part of the shape. The proposed descriptor consists of Zernike moments of outer and inner parts. Euclidean distance is used for computing the distance measure between two shapes. For perceptual similarity-based retrieval, the Zernike moments of the outer part are used and for exact-matching retrieval, both of the outer and inner Zernike moments are used. Experimentation under various test conditions shows the effectiveness of the proposed modified Zernike moment descriptor.