Hybrid Tile Based Feature Extraction and Support Vector Machine Base Content-Based Image Retrieval System for Medical Application
M. Malathy1, C. Rajinikanth2, V. Mohan3, T. Yuvaraja4
1Dr. M. Malathy Professor, Dept of Information Technology, Veltech High Tech Engineering College, Chennai.
2C. Rajinikanth Dept of Electronics and Instrumentation Engineering, Annamalai University.
3Dr. V. Mohan Associate Professor, Dept of Electronics and Communication Engineering, Saranathan College of Engineering, Trichy.
4T. Yuvaraja, Assistant Professor, Dept of Electronics and Communication Engineering Kongunadu College of Engineering and Technology, Thottiam,Trichy .
Manuscript received on 6 August 2019. | Revised Manuscript received on 12 August 2019. | Manuscript published on 30 September 2019. | PP:3305-3310 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5433098319/2019©BEIESP | DOI: 10.35940/ijrte.C5433.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Through the landing of therapeutic endoscopes, earth perception satellites and individual telephones, content-based picture recovery (CBIR) has concerned critical consideration, activated by its broad applications, e.g., medicinal picture investigation, removed detecting, and individual re-distinguishing proof. Be that as it may, developing successful component extraction is as yet reported as an invigorating issue.In this paper, to overcome the feature extraction problems a hybrid Tile Based Feature Extraction (TBFE) is introduced. The TBFE algorithm is hybrid with the local binary pattern (LBP) and Local derivative pattern (LDP). These hybrid TBFE feature extraction method helps to extract the color image features in automatic manner. Support vector machine (SVM) is used as a classifier in this image retrieval approach to retrieve the images from the database. The hybrid TBFE along with the SVM classifier image retrieval is named as IR-TBFE-SVM. Experiments show that IR-TBFE-SVMdelivers a higher correctness and recall rate than single feature employed retrieval systems, and ownsdecentweight balancing and query efficiency performance.
Index Terms: Content-based image retrieval (CBIR), hybrid Tile Based Feature Extraction (TBFE), local binary pattern (LBP), Local derivative pattern (LDP) and Support vector machine (SVM).
Scope of the Article: Machine Design