Abstract
In various domains like security, education, biomedicine etc., the volume of digital data is increasing rapidly, and this is becoming a challenge to retrieve the information from the storage media. Content-based image retrieval systems (CBIR) aim at retrieving from large image databases the images similar to the given query image based on the similarity between image features. This paper aim to discuss and solve the problem of designing sub-block features based image retrieval. Firstly, this paper outlines a description of the primitive features of an image. Then, the proposed methodology for partitioning the image and extracting its colour and texture is described. The algorithms used to calculate the similarity between extracted features, are then explained. Finally, we compared with some other existing CBIR methods, using the WANG database, which is widely used for CBIR performance evaluation, and the results demonstrate the proposed approach outperforms other existing methods considered.
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