Feature-based Texture Feature Extraction, Classification And Retrieval Method

Texture is one of the essential and important characteristics of imagery. As an important component of image understanding, analysis and recognition, texture analysis is valuable not only in theory, but also in wide-range applications. There are two basic types of methods in texture analysis which are the method based-on feature and the method based-on model. Texture analysis composes of texture description, texture segment, texture classification, texture retrieval, etc. In this paper, some approaches on texture feature extraction for purpose of classification and retrieval are discussed and studied.The major work implemented in this paper is presented as follows:(1) An improved discrete fast polar Fourier transform algorithm (called DPFT for short) is proposed which fast and effectively computes the 2D discrete Fourier transform in polar coordinate systems. And based on it, a new texture classification method in presented that utilizes the information of the DPFT serialized amplitude spectrum in different scales and orientations as texture features, and has good performance in texture classification. Because the Fourier transform is widely applied in engineering and the polar coordinate systems has superiority in data' rotation and scaling, the improved DPFT algorithm can also be implemented in many fields such as image registration, image retrieval and radar signal processing.(2) A new texture classification algorithm based-on the lifting wavelet transform in suggested utilizing wavelets' perfect performance in spatial-frequency localization and the lifting wavelets' in-place operations. It computes the entropy values of the sub-frequency images disposed by the two-layers' lifting wavelet transform, and has merits of low feature dimensions, low computational cost and good capability.(3) An algorithm for classification of texture image based-on spectrum histogram and self-organized feature mapping network in proposed. It induces the idea of pixel's 8-neighbor Fourier series, randomly chooses the local region of texture and computes the spectrum of it. After quantizing the spectrum, the spectrum histogram of the local area is extracted and then provided to the SOFM network as a feature vector to train the net. The neurons in the topological output layer correspond to different textures when the training process is finished. Experiments on six samples of Brodatz textures demonstrate the simplicity and efficiency of this algorithm.(4) Approaches for texture retrieval based on high-order cumulants, multi-channel Gabor filters and lifting wavelet transform are suggested separately which extract the texture features to implement the texture queries. In these methods, the approach based-on high-order cumulants utilize the local window's correlation of image to compute the three-order cumulants as features. And the other two methods are perfect in spatial-frequency localization. In a whole these twospatial-frequency localized methods are more effective than the method based-on high-order cumulants for the superiority in embodying images' information at different scales and orientations.The texture' test database in this paper is composed of 108 different kinds of Brodatz textures.