Image retrieval using edge based shape similarity with multiresolution enhanced orthogonal polynomials model

Since shape is one of the important low level features of any Content Based Image Retrieval (CBIR) system, this paper proposes a new edge based shape feature representation method with multiresolution enhanced orthogonal polynomials model and morphological operations for effective image retrieval. In the proposed method, initially the orthogonal polynomials model coefficients are computed and reordered into multiresolution subband like structure. Edge image is then obtained by utilizing the two level adaptive thresholds and local maxima of the gradient in horizontal, vertical, diagonal and anti-diagonal directions. The approximate shape boundary of the image is recovered with morphological operations. Then the Pseudo Zernike moment based global shape features, which are invariant to basic geometric transformations, are extracted. The obtained features are termed as global shape feature vector and are used for retrieving similar images with Canberra distance metric. The efficiency of the proposed method is experimented on a subset of standard Corel, Yale and MPEG-7 databases and the results are compared with existing techniques.