Evaluation of Texture Methods for Image Analysis

The evaluation of texture features is important for several image processing applications. Texture analysis forms the basis of object recognition and classification in several domains. There is a range of texture extraction methods and their performance evaluation is an important part of understanding the utility of feature extraction tools in image analysis. In this paper we evaluate five different feature extraction methods. These are auto-correlation, edge frequency, primitive-length, Law’s method, and co-occurrence matrices. All these methods are used for texture analysis of Meastex database. This is a publicly available database and therefore a meaningful comparison between the various methods is useful to our understanding of texture algorithms. Our results show that the Law’s method and co-ccurrence matrix method yield the best results. The overall best results are obtained when we use features from all five methods. Results are produced using leave-one-out method.