S M Rafizul Haque

Work place: Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh

E-mail: rafizul@cse.ku.ac.bd


Research Interests: Computational Learning Theory, Computer Vision, Image Processing, Data Structures and Algorithms


S M Rafizul Haque is a Professor in the Department of Computer Science and Engineering at Khulna University, Khulna, Bangladesh. Currently, he is working as a research scientist in Canadian Food Inspection Agency. He received his PhD from University of Saskatchewan, Canada. He received his B.Sc. degree in Computer Science and Engineering from Khulna University,

Bangladesh and M.Sc. degree in Computer Science from Blekinge Institute of Technology, Sweden. His research interests include image processing, computer vision, machine learning and data science. He has published several research papers in international journals and conferences.

Author Articles
Content-Based Image Retrieval Using Color Layout Descriptor, Gray-Level Co-Occurrence Matrix and K-Nearest Neighbors

By Md. Farhan Sadique S M Rafizul Haque

DOI: https://doi.org/10.5815/ijitcs.2020.03.03, Pub. Date: 8 Jun. 2020

Content-based image retrieval (CBIR) is the process of retrieving similar images of a query image from a source of images based on the image contents. In this paper, color and texture features are used to represent image contents. Color layout descriptor (CLD) and gray-level co-occurrence matrix (GLCM) are used as color and texture features respectively. CLD and GLCM are efficient for representing images with local dominant regions. For retrieving similar images of a query image, the features of the query image is matched with that of the images of the source. We use cityblock distance for this feature matching purpose. K-nearest images using cityblock distance are the similar images of a query image. Our CBIR approach is scale invariant as CLD is scale invariant. Another set of features, GLCM defines color patterns. It makes the system efficient for retrieving similar images based on spatial relationships between colors. We also measure the efficiency of our approach using k-nearest neighbors algorithm. Performance of our proposed method, in terms of precision and recall, is promising and better, compared to some recent related works.

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