Image Classification Using Non-Homogenous Textures Analysis

Abstract

Texture analysis and classification are usual tasks in pattern recognition. In this paper, a
texture classification method is used, which is based on textural features of the object. The textural
features are calculated from the co-occurrence matrix. In the case of natural images, the feature
distributions are often non-homogenous and the image classes are also overlapping in the feature space.
This can be problematic, if all the descriptors are combined into a single feature vector in the
classification. A method is presented for combining different visual descriptors in image classification. In
this approach, k-nearest neighbour classification is first carried out for each descriptor separately. After
that, the final decision is made by combining the nearest neighbours in each base classification. The total
numbers of the neighbours representing each class are used as votes in the final classification.