Content-Based Image Retrieval with Combined Features: Color and Gradient

Abstract

\n Feature extraction is fundamental stage of effective content based image retrieval (CBIR). However, it remains challenging issue to extract low-level features for retrieval systems. This paper puts forward an effective solution proposal for the aforementioned problem. Initially, images and their gradients are clustered with multi-level thresholding. A codebook is generated with threshold values. The size of the codebook generated depends on the number of thresholds. Consequently, every pixel in color image is included in a cluster by means of the codebook. Color reduction is performed by assigning the average values of pixels in the same cluster. A cluster-based one-dimensional histogram (CBH) is created with the numbers of pixels in every cluster represented with a single color. Then the cluster-based feature vectors with histogram are extracted from original image and gradient image. Accordingly, relevant features are combined. The developed feature vector is called as combined feature vector (CFV). The most important advantages of CFV are that it performs an effective color reduction technique and feature presentation by processing texture information with gradient operator. Therefore, the main contribution of the combined feature vector suggested is its high accuracy and stability for image retrieval. The proposed method has been tested with Corel-1K, Corel-5K Corel-10K and GHIM-10K datasets. In addition, performances of different image histogram similarity techniques such as cosine, histogram intersection and Euclidean distance have been verified with the developed algorithm. Experimental results have been analyzed in two categories. Initially, CBIR results produced with combined feature vectors which are generated by Otsu, Kapur and center of gravity of histogram (CGH) procedures have been evaluated. Then, the CBIR strategy based on CGH method has been compared with CBIR systems with local binary pattern (LBP) and gradient-structures histogram (GSH). It was observed that CBIR approach based on CGH technique has significantly outperformed.