Texture Image Classification Using Visual Perceptual Texture Features and Gabor Wavelet Features

Abstract

Texture can describe a wide variety of surface characteristics and a key component for human visual perception and plays an important role in image-related applications. This paper proposes a scheme for texture image classification using visual perceptual texture features and Gabor wavelet features. Three new texture features which are proved to be in accordance with human visual perceptions are introduced. Usually, Subband statistics based on Gabor wavelet features are normally used to construct feature vectors for texture image classification. However, most previous methods make no further analysis of the decomposed subbands or simply remove most detail coefficients. The classification algorithms commonly use many features without consideration of whether the features are effective for discriminating different classes. This may produce unnecessary computation burden and even decrease the retrieval performance. This paper proposes a method for selecting effective Gabor wavelet subbands based on feature selection functions. The method can discard those subbands that are redundant or may lead to wrong classification results. We test our proposed method using the Brodatz texture database, and the experimental results show the scheme has produced promising results.