Performance Comparison of Various Feature Descriptors in Object Category Detection Application using SVM Classifier
Kavitha B. R1, Ramya G2, Priya G3
1Kavitha B.R, Department of Information Technology and Engineering, SITE, Vellore Institute of Technology, Vellore (A.P), India.
2Ramya G, Department of Information Technology and Engineering, SITE, Vellore Institute of Technology, Vellore (A.P), India.
3Priya G, Department of Computing Science and Engineering, SCOPE, Vellore Institute of Technology, Vellore (A.P), India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 461-464 | Volume-8 Issue-5, March 2019 | Retrieval Number: E3091038519/19©BEIESP
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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: Feature extraction involves feature detection, description and matching which is the baseline of many computer vision applications like content based image retrieval, image classification, image recognition, object detection etc. Features detected should have greater repeatability and should be able to derive descriptors out of it that are highly distinctive and robust to changes in scale, orientation, rotation, illumination etc. This paper provides an insight about the performance comparison of the long existing SIFT and SURF descriptors. The evaluation is carried out in an experimental setup of object category detection which uses a SVM classifier to detect the category.
Keyword: Feature Detectors, Descriptors, SIFT, SURF, ORB, BRISK, Bag-Of-Features.
Scope of the Article: RFID Network and Applications