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Performance comparison of various feature descriptors in object category detection application using SVM classifier
Published in Blue Eyes Intelligence Engineering and Sciences Publication
Volume: 8
Issue: 5
Pages: 461 - 464
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. © BEIESP.
About the journal
JournalInternational Journal of Innovative Technology and Exploring Engineering
PublisherBlue Eyes Intelligence Engineering and Sciences Publication