Zernike moments and LDP-weighted patches for content-based image retrieval

This paper proposes a new algorithm using global and local features for content-based image retrieval. Global features are extracted using the magnitude of Zernike moments (ZMs). Local features are obtained through local directional pattern (LDP). Generally, LDP is used to extract texture-based features from an image. In this paper, LDP is used to encode both texture and shape information of an image to represent more meaningful features. To encode texture-based features, original image is used to compute the LDP features. To extract shape information from an image, dual-tree complex wavelet transform (DT-CWT) is applied on image which generates six directional wavelets. These six directional wavelets are superimposed in order to obtain shape-encoded image. LDP is then applied on this wavelet-based shape-encoded image. Further, to enhance retrieval accuracy, LDP features are extracted from patches of both original and shape-encoded images. These patches are assigned with weights based on average discrimination capability of features in a patch. Experiments are performed using three different standard databases with various variations such as pose, distortion, partial occlusion and complex structure. The proposed technique achieves 96.4 and 98.76 % retrieval accuracy at a recall of 50 %, for Kimia-99 and COIL-100 databases, respectively. For MPEG-7 CE-2 shape database, retrieval accuracy of 61.93 % is achieved in terms of average Bull’s eye performance (BEP). The proposed technique is also tested on Springer medical image database to explore its scope in other areas, wherein it attains average BEP of 69.68 % in comparison with 61.52 % with ZMs. It is observed that the proposed technique outperforms other well-known existing methods of image retrieval.