期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2013
卷号：4
期号：7-2
出版社：Seventh Sense Research Group
摘要：Continuous extension of digital images requires new methods for sorting, browsing, and searching through huge image databases. Texture classification is very important in image analysis. Content based image retrieval, examination of surfaces, object detection by texture, document segmentation are only some examples where texture classification plays a major role. This is a domain of ContentBased Image Retrieval (CBIR) systems, which are database search engines for images. A user typically submits a query image or series of images and the CBIR system tries to find and to retrieve the most similar images from the database. Optimally, the retrieved images should not be sensitive to circumstances during their acquisition. Unfortunately, the appearance of natural objects and materials is highly illumination and viewpoint dependent. Classification of texture images, especially those with different direction and illumination changes, is a challenging and important problem in image analysis and classification. Here we propose an effective scheme for representing and retrieval of homogeneous image called texture, under the circumstances with variable illumination and texture rotation. For rotation and illumination invariant feature extraction we used Local Binary Pattern method with rotation and illumination invariance and for classification we used Support Vector Machine and k Nearest neighbor method. The experimental results based on Outex dataset with different rotations and illuminations.
