Content-Based Image Retrieval using Local Binary Curvelet Co-occurrence Pattern—A Multiresolution Technique. (20th September 2017)

• Record Type: Journal Article
• Authors: Srivastava, Prashant; Khare, Ashish
• Is Part Of: Computer journal. Volume 61:Number 3(2018)
• Page Start: 369
• Page End: 385
• Publication Date: 2017-09-20
• Subjects: content-based image retrieval -- multiresolution analysis -- curvelet transform -- feature vector -- GLCM; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxx086
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: With the growth of various image-capturing devices, image acquisition is no longer a difficult task. As this technology is flourishing, various types of complex images are being produced. In order to access a large number of images stored in database easily, the images must be properly organized. Field of image retrieval attempts to solve this problem. As the complex images are being produced, processing them using single-resolution techniques is not sufficient as these images may contain varying levels of details. This paper proposes a novel multiresolution descriptor, local binary curvelet co-occurrence pattern, to achieve the task of content-based image retrieval. Curvelet transform of grayscale image is computed followed by computation of local binary pattern of resulting curvelet coefficients. Finally, feature vector is constructed using grey-level co-occurrence matrix which is matched with the feature vector of database images. The proposed descriptor combines the properties of local pattern and multiresolution technique of curvelet transform, and efficiently covers curvilinear and geometrical structures present in the image. Performance of the proposed method is measured in terms of precision and recall and is tested on five benchmark datasets consisting of natural images. The proposed method has been compared with single and multiresolution techniques as well as with some of the other state-of-the-art image retrieval methods. The experimental results clearly demonstrate that the proposed method produces high retrieval accuracy and outperforms other techniques in terms of precision and recall.