A Novel Content-based Image Retrieval System using Fusing Color and Texture Features

Content-based image retrieval (CBIR) systems compare a query image with images in a dataset to find similar images to a query image. In this paper, a novel and efficient CBIR system is proposed using the color and texture features. The color features are represented by color moments and color histograms of RGB and HSV color spaces, and the texture features are represented by localized Discrete Cosine Transform (DCT) and localized gray level co-occurrence matrix and local binary patterns (LBPs). The DCT coefficients and gray level co-occurrence matrix of the blocks are examined for assessing the block details. Also, LBP is used for rotation invariant texture information of the image. After feature extraction, the Shannon entropy criterion is used to reduce the inefficient features. Finally, an improved version of Canberra distance is employed to compare the similarity of feature vectors. The experimental analysis is carried out using precision and recall on the Corel-5K and Corel-10K datasets. The results demonstrate that the proposed method can efficiently improve the precision and recall, and outperforms the most existing methods.