Statistical Texture Representation Based On The Distribution Of Local Structure

Texture analysis is an old but long lasting research area for computer vision, and the texture representation is an important step for most applications. Texture description,unlike other forms of representation where the objects have a definite structure, most textures have large stochastic variations and quasi-periodicity structures which make them difficult to model. Therefore, local statistical representation is a hot topic for texture representation. In order to obtain the statistical representation, each local structure should be labeled distinctively. Specifically, two fundamental problems to label the local structures are: first, how to generate local descriptor to describe local structures with higher distinguishing ability, lower redundancy and imaging conditions invariance; second, how to quantize the local descriptor distinctively(by vector quantization) to preserve more structure information. Addressing to the two problems, our research focuses on the following parts:(1) Addressing to the problem of how to generate local descriptor, we use normalized local-oriented energies to generate local descriptor, which describe the local structures distinctively and are less sensitive to imaging conditions. Then, addressing to the problem of vector quantization, each local descriptor is quantized by self-adaptive quantization thresholds determined in the learning stage using histogram equalization, and the quantized local feature vector is transformed to a number by N-nary coding, which helps to preserve more structure information during vector quantization. Finally, the frequency histogram is used as the representation feature. Furthermore, according to the selection of dominant orientation and combination of histogram bins, the representation achieves rotation invariance. By using pyramid representation and shifting strategy for calculating dissimilarity, the proposed method obtains the scale invariance. The performance was benchmarked by material categorization on KTH-TIPS and KTH-TIPS2-a databases. The proposed method was compared with typical statistical approaches. The results show that our method is superior to other methods on the KTH-TIPS2-a database, and achieving competitive performance on the KTH-TIPS database. Besides, we preliminary attempt to use the proposed method for auroral image classification. The results lay the foundation for automatical classification of massive auroral images.(2) Texton dictionary-based texture representation approaches have been proven to be effective for texture classification. We propose two types of local descriptors based on Gaussian derivatives filters, both of which have the property of continuous rotation invariance. The first descriptor directly uses the maximum of the filter responses named as continuous maximum responses(CMR). The second descriptor rectifies the filter responses to calculate principal curvatures(PC) of the image surface. The texton dictionary is learned from the training images by clustering the local descriptors, and the representation of each image is the frequency histogram of the textons. The classification results compared with some other popular methods on the CUReT, KTH-TIPS and KTH-TIPS2-a datasets show that representation based on CMR achieves best classification result on the CUReT dataset. The representation based on PC achieves the best classification results on the KTH-TIPS and KTH-TIPS2-a datasets, and the classification performance is robust on different datasets. The experiments of rotation invariant analysis implemented on the Brodatz album illustrate that the CMR descriptor has good inter-class distinguish ability and PC descriptor has strong intra-class congregate ability. The results demonstrate that the proposed local descriptors achieve remarkable performance to classify the rotated textures.(3) This paper proposes an effective scale invariant texture representation based on frequency decomposition and gradient orientation. First, the image intensities are decomposed into different orientations by using the wedge filters in frequency domain, and the N-nary coding method is adopted for the vector quantization. Second, the scale invariant gradient orientation is generated by selecting the most stable value of the gradient orientation with different Gaussian scales. Finally, the 2D joint distribution of the two types of local descriptors is used as the representation. The performance was evaluated by the texture classification using nearest neighbor classifier. Simple but not ordinary, our method achieves the state of the art classification performance on the KTH-TIPS dataset under the traditional experimental design. Moreover, the main experiments were conducted on the KTH-TIPS and KTH-TIPS2-b datasets with the experimental designs of scale invariance validation. Compared with the methods of basic image features(BIFs) and local energy pattern(LEP), the proposed representation achieves superior performance with a much lower dimension of representation.(4) Addressing to the problem of vector quantization, we propose a fast vector quantization(VQ) method for statistical texture representation by combining two approaches of codebook and N-nary coding. Compared with the codebook, the proposed method offers a more efficient way of finding the nearest texton for the local descriptor, which does not need to calculate the distances between textons one by one, since a mapping procedure avoids the time-consuming calculation of the codebook. Compared with the N-nary coding, the proposed method relabels the coding numbers, which reduces the dimension of representation and makes it possible to quantize relatively higher dimensional local descriptor. In the experiment of texture classification, by using different sorts of local descriptors, the classification performance of proposed method does not decrease too much,on a contrary, it is more moderate and stable. Above all, it is estimable for our method to obtain stable performance with high efficiency and low dimension simultaneously, which suggests its practical application value.(5) Since the local energy pattern(LEP) achieves competitive classification performance for two dimensional texture image, we extend the method for three dimensional dynamic texture representation motivated by three dimensional local binary pattern. We extract the volume LEP(VLEP) by considering the dynamic texture sequence as three dimensional data.The 3D steerable filters replace 2D steerable filters to capture the texture and motion information simultaneously.