Yongsheng Dong

Learn More
In this letter, we propose an efficient one-nearest-neighbor classifier of texture via the contrast of local energy histograms of all the wavelet subbands between an input texture patch and each sample texture patch in a given training set. In particular, the contrast is realized with a discrepancy measure which is just a sum of symmetrized Kullback-Leibler(More)
Statistical modeling of wavelet subbands has frequently been used for image recognition and retrieval. However, traditional wavelets are unsuitable for use with images containing distributed discontinuities, such as edges. Shearlets are a newly developed extension of wavelets that are better suited to image characterization. Here, we propose novel texture(More)
The pretreatment of raw materials is necessary for ethanol production from lignocellulose, however, a variety of compounds which inhibit the fermenting microorganism such as Saccharomyces cerevisiae are inevitably formed in this bioprocess. Based on their chemical properties, the inhibitors are usually divided into three major groups: weak acids,(More)
As a newly developed 2-D extension of the wavelet transform using multiscale and directional filter banks, the contourlet transform can effectively capture the intrinsic geometric structures and smooth contours of a texture image that are the dominant features for texture classification. In this paper, we propose a novel Bayesian texture classifier based on(More)
Non-negative matrix factorization (NMF) has been one of the most popular methods for feature learning in the field of machine learning and computer vision. Most existing works directly apply NMF on high-dimensional image datasets for computing the effective representation of the raw images. However, in fact, the common essential information of a given class(More)
Effective representation of image texture is important for an image-classification task. Statistical modelling in wavelet domains has been widely used to image texture representation. However, due to the intraclass complexity and interclass diversity of textures, it is hard to use a predefined probability distribution function to fit adaptively all wavelet(More)
This paper presents a new approach to extract image features for texture classification. The extracted features are obtained by a dominant-completed modeling of the traditional local binary pattern (LBP) operator, which is robust to image rotation, grey scale changing and insensitive to noise and histogram equalization. The main idea of this texture(More)