AN EFFICIENT CONTENT BASED IMAGE RETRIEVAL USING COLOR AND TEXTURE OF IMAGE SUBBLOCKS

摘要

Image retrieval is an active research area in image processing, pattern recognition, and computer vision. For the purpose of effectively retrieving more similar images from the digital image databases, this paper uses the local HSV color and Gray level co-occurrence matrix (GLCM) texture features. The image is divided into sub blocks of equal size. Then the color and texture features of each sub-block are computed. Color of each sub-block is extracted by quantifying the HSV color space into non-equal intervals and the color feature is represented by cumulative color histogram. Texture of each sub-block is obtained by using gray level co-occurrence matrix. An integrated matching scheme based on Most Similar Highest Priority (MSHP) principle is used to compare the query and target image. The adjacency matrix of a bipartite graph is formed using the sub-blocks of query and target image. This matrix is used for matching the images. Euclidean distance measure is used in retrieving the similar images. As the experimental results indicated, the proposed technique indeed outperforms other retrieval schemes in terms of average precision.