IMAGE TEXTURE CLASSIFICATION: SURF WITH SVM

Authors

  • MISS. NAMRATA N. RADE Department of Electronics and Telecommunication Engineering, Bharati Vidyapeeth’s College of Engineering, Kolhapur, India
  • PROF. J. K. PATIL Department of Electronics and Telecommunication Engineering, Bharati Vidyapeeth’s College of Engineering, Kolhapur, India

Keywords:

Classification, Description, Extraction, Key-points

Abstract

Nowadays, various approaches of texture classification have been developed which works on acquiredimagefeatures and separate them into different classes by using a specificclassifier. This paper gives a state-of-the-arttextureclassificationtechnique called Speeded up Robust Features (SURF) with SVM(Support Vector Machine)classifier. In this concept, image data representation is accomplished by capturing features in the form of key-points. SURF uses determinant of Hessian matrix to achieve point of interests on which description and classification is carried out.This method gives superiorperformance over already established methods in terms of processing time,accuracy and robustness. In this paper, we have taken UMD datasetfor processingand calculated different performance parameters which gives excellent results.

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Published

2021-03-27

Issue

Vol. 4 No. 5 (2017): IJIERT

Section

Articles

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