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

Abstract

ABSTRACTFeature 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.