An empirical evaluation of recent texture features for the classification of natural images

ABSTRACT

Feature extraction and classification are considered to be major tasks in image processing applications. This paper presents the performance of three feature extraction algorithms: the Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Dominant Rotation Local Binary Pattern (DRLBP). The SIFT is robust to occlusion and clutter since it considers local features. The SURF is used to generate features for small objects and the extraction of interest points is also faster. The DRLBP is computationally efficient, given that it considers dominant and frequently occurring patterns. The texture performance of all these descriptors is measured in terms of accuracy on rotation invariant, scale invariant, and illumination invariant images taken from the Brodatz and Outex texture image databases. The texture images are classified using the K-Nearest Neighbor (KNN) and Naïve Bayes classifiers. The experimental result shows that the DRLBP with the KNN classifier provides better classification accuracy against various challenges.

KEYWORDS:

• Scale Invariant Feature Transform
• Speeded-Up Robust Features
• Dominant Rotation Local Binary Pattern
• K-Nearest Neighbor
• Naïve Bayes

Additional information

Notes on contributors

A. Suruliandi

Dr. A. Suruliandi is a Professor in Computer Science & Engineering in Manonmaniam Sundaranar University, Tirunelveli. He received his B.E Degree in Electronics & Communication Engineering from Coimbatore Institute of Technology, Tamilnadu in 1987. He received his M.E Degree in Computer Science and Engineering in 2000 and Ph.D, Computer Science & Engineering in 2009 from Manonmaniam Sundaranar University, Tirunelveli. He has been a member of Board of Studies in various colleges across Tamilnadu. His research area includes Digital image Processing, Remote Sensing, Pattern Recognition, Data Mining and Data Compression. He is a reviewer of an international journal IET-Computer Vision. He has published over 90 research papers in these areas.

J.C. Kavitha

J. C. Kavitha received her B.E Degree in Computer Science & Engineering from Bharathiar University, Tamilnadu in 2000. She received her M.Tech Degree in Computer Science & Engineering from Uttar Pradesh Technical University, Lucknow in 2011. She is currently pursuing Ph.D Degree in Computer Science & Engineering from Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu. Her research interests include Image Processing, Medical Informatics, Network Security, Cloud Computing and Pattern Recognition.

D. Nagarajan

Dr. D. Nagarajan is a Professor in Department of Mathematics in Hindustan Institute of Technology & Science, Chennai, Tamilnadu. He received his M.Sc Degree from Manonmaniam Sundaranar University, Tirunelveli. He received his Ph.D degree in Mathematics-Statistics in 2007 from Manonmaniam Sundaranar University, Tirunelveli. His research areas include Image Processing, Stochastic Process and Data mining. He has published over 40 research papers in these areas. He is a peer reviewer of The research council [TRC], Sultanate of Oman.