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Enhancing the Precision of Walsh Wavelet Based Approach for Color and Texture Feature Extraction in CBIR by Including a Shape Feature

Abstract This paper proposes a method for enhancing the performance of Content Based Image Retrieval, employing a shape feature along with color and texture of Walsh wavelet. The color and texture features of the images are extracted using Walsh Wavelet and the shape feature is extracted by edge detection using any of Roberts, Sobel, Prewitt or Canny Operator. The performance of the approach is tested based on the precision values on a database containing 44 images. The results show that the precision of retrieval is increased when a shape feature is employed in the second stage of a two-stage retrieval process. Adding the shape as a third feature in a single stage retrieval process does not provide any improvement in retrieval performance with respect to precision and recall. Performance comparison was also carried out with other existing approaches, namely Walshlet and Walsh transform. The experimental results show that Walsh Wavelet has higher precision than Walshlet and Walsh transform. Also, shape extraction with Sobel and Prewitt operators provides better performance when compared to Canny and Roberts.