Abstract—Background: the considerable time consumption, query retrieval difficulty and reduced retrieval rate. Still remaining challenges in Content-based image retrieval.
Methods: in this work, we propose a pre-processing method that uses a Gaussian filter to improve quality by reducing image noise. An effective feature extraction method for in presented to extracted texture help color co-occurrence feature (CCF), color and shape features such as area and diameters. The colors features are extracted by means of a grey-level co-occurrence matrix and bit pattern. Extracting these features will enhance the image retrieval accuracy. With the use of a novel multi-SVM classifier, classification is performed and image retrieval is completed effectively.
Results: performance measures, namely, precision, recall, error rate, correct rate, and retrieval rate, are computed. The proposed methodology produces superior results on these measures and exhibits an effective retrieval rate of approximately 94.92%; therefore, our technique is more efficient than existing MRED and MALP methods.
Index Terms—Pre-processing, feature extraction, novel Multi-SVM classifier, color co-occurrence feature (CCF), grey level co-occurrence feature (GLCM), bit pattern feature (BPF).
Mudhafar J. Alghrabat, Guangzhi Ma, Paula Leticia Pinon Avila are with Huazhong University of Science and Technology, Wuhan, China (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org). Muna Jalil Jassim was with the Alrafidain University College, School Computer Engineering, Baghdad, Iraq (e-mail: email@example.com).
Safa J. Jassim is with the Electrical Engineering Department, Al-Mustansiriya University, Baghdad, Iraq (e-mail: firstname.lastname@example.org).
Cite: Mudhafar J. J. Ghrabat, Guangzhi Ma, Paula Leticia Pinon Avila, Muna J. Jassim, and Safa J. Jassim, "Content-based Image Retrieval of Color, Shape and Texture by Using Novel multi-SVM Classifier," International Journal of Machine Learning and Computing vol. 9, no. 4, pp. 483-489, 2019.Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).