Abstract
Content-based image retrieval (CBIR), also known as query by image content is one of the applications of computer vision. In CBIR, the contents derived from the image like color, shapes, and textures are analyzed rather than the metadata such as keywords, tags, and/or descriptions associated with the image. In this paper, the texture features of the image are modelled using Generalized Gaussian Distribution (GGD) and Ripplet Transform. Ripplet transform has the capability of capturing structural information along with the curves compared to the traditional discrete wavelet transform. The similarity between query image and the training database is found using Kullback-Leibler Distance between GGDs. The proposed system provides greater accuracy and flexibility in capturing texture information. Experimental results on a large image database demonstrate the efficiency and effectiveness of the proposed CBIR system in the image retrieval paradigm.
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