Journal Press India^®

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Content-based image retrieval (CBIR) is one of the most practiced areas in recent times. In a CBIR system, each image is represented using its primitive features like color, texture, and shape. These primitive features are extracted in various ways to form the final feature vector and used for image retrieval based on a selected similarity measurement. The size of a feature vector is also one of the deciding factors in computation time. Hence, smaller-sized feature vectors with comparable results are always advised. In this paper, to make the system closer to human perception, we have used the HSV counterpart. As all the pixel values do not contain vital information and unnecessarily increase computational overhead. Hence, in this work, we have used mid-rise quantization. In this work, we have used local statistical parameters to evaluate the color information. For texture extraction, GLCM is employed, and the shape feature is extracted using adaptive tetrolet transformation. We have validated our system using various widely accepted benchmark databases. The retrieved results are capable enough to demonstrate its improvement over other works.

Keywords

Content-Based Image Retrieval (CBIR); Mid-Rise Quantization; GLCM; Tetrolet

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