一种基于二维局部二值模式的纹理图像分类方法

局部二值模式（Local Binary Pattern,LBP）在纹理分类中受到越来越多的关注,传统的基于局部二值模式的图像识别方法在LBP直方图统计时仅仅考虑到LBP模式值本身的数量统计,却忽略了模式值之间的相关性.针对这一问题,本文提出一种二维局部二值模式（Two Dimensional Local Binary Pattern,2DLBP）方法,并用于纹理图像识别.首先以旋转不变均匀LBP特征图为基础,引入滑动窗口和LBP模式对的概念,统计LBP模式图的上下文信息,构造出2DLBP特征;然后改变LBP中的半径参数,构造图像的多分辨率2DLBP特征,并利用支持向量机（SVM）的分类方法进行纹理分类;最后选取Brodatz、CUReT、UIUC、FMD四个公开纹理库分别进行纹理分类测试.理论验证表明该方法具有良好的通用性,可以与LBP的其他变型结合成为新的图像特征构造方法.同时,实验结果表明,本文提出方法具有较好的纹理图像分类能力.     

An improved LBP algorithm for texture and face classification

Local Binary Pattern (LBP) has achieved great success in texture classification due to its accuracy and efficiency. Traditional LBP method encodes local features by binarying the difference in local neighborhood and then represents a given image using the histogram of the binary patterns. However, it ignores the directional statistical information. In this paper, some directional statistical features—including the mean and standard deviation of the local absolute difference—are integrated into the feature extraction to improve the classification ability of the extracted features. In order to reduce estimation errors of the local absolute difference, we further utilize the least square estimate technique to optimize the weight and minimize the local absolute difference, which leads to more stable directional features. In addition, a novel rotation invariant texture classification approach is presented. Experimental results on several texture and face datasets show that the proposed approach significantly improves the classification accuracy of the traditional LBP.     

基于金字塔分解和扇形局部均值二值模式的鲁棒纹理分类方法

针对传统局部二值模式(LBP)的特征鉴别力有限和噪声敏感性问题,该文提出一种基于金字塔分解和扇形局部均值二值模式的纹理特征提取方法。首先,将原始图像进行金字塔分解,得到对应于不同分解级别的低频和高频(差分)图像。为提取兼具鉴别力和稳健性的特征,进一步采用阈值化处理技术将高频图像转化为正、负高频图。然后,基于局部均值操作提出一种扇形局部均值二值模式(SLMBP),用于计算各级分解图像的纹理特征码。最后,对纹理特征码进行跨频带的联合编码和跨级别的直方图加权,从而获得最终的纹理特征。在公开的3个纹理数据库(Outex, Brodatz和UIUC)上进行分类实验,结果表明该文所提方法能够有效地提高纹理图像在无噪声环境和含高斯噪声环境下的分类精度。     

Color local texture features for color face recognition

This paper proposes new color local texture features, i.e., color local Gabor wavelets (CLGWs) and color local binary pattern (CLBP), for the purpose of face recognition (FR). The proposed color local texture features are able to exploit the discriminative information derived from spatiochromatic texture patterns of different spectral channels within a certain local face region. Furthermore, in order to maximize a complementary effect taken by using both color and texture information, the opponent color texture features that capture the texture patterns of spatial interactions between spectral channels are also incorporated into the generation of CLGW and CLBP. In addition, to perform the final classification, multiple color local texture features (each corresponding to the associated color band) are combined within a feature-level fusion framework. Extensive and comparative experiments have been conducted to evaluate our color local texture features for FR on five public face databases, i.e., CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0. Experimental results show that FR approaches using color local texture features impressively yield better recognition rates than FR approaches using only color or texture information. Particularly, compared with grayscale texture features, the proposed color local texture features are able to provide excellent recognition rates for face images taken under severe variation in illumination, as well as for small- (low-) resolution face images. In addition, the feasibility of our color local texture features has been successfully demonstrated by making comparisons with other state-of-the-art color FR methods.     

Integrated spectral and spatial information mining in remote sensing imagery

Most existing remote sensing image retrieval systems allow only simple queries based on sensor, location, and date of image capture. This approach does not permit the efficient retrieval of useful hidden information from large image databases. This paper presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed imagery using state-of-the-art data mining and advanced database technologies. Land cover information corresponding to spectral characteristics is identified by supervised classification based on support vector machines with automatic model selection, while textural features characterizing spatial information are extracted using Gabor wavelet coefficients. Within identified land cover categories, textural features are clustered to acquire search-efficient space in an object-oriented database with associated images in an image database. Interesting patterns are then retrieved using a query-by-example approach. The evaluation of the study results using coverage and novelty measures validates the effectiveness of the proposed remote sensing image information mining framework, which is potentially useful for applications such as agricultural and environmental monitoring.     

Dimensionality reduction-based feature extraction and classification on fleece fabric images

This work performs dimensionality reduction-based classification on fleece fabric-based images taken by a thermal camera. In order to convert images into the gray level, a principal component analysis-based dimension reduction stage was proposed. In addition, symmetric central local binary patterns were performed with the help of the proposed method by using the images after dimension reduction process. The local binary pattern features preserve local texture features from different kinds of defective image types. The experimental results showed that combined work has a great classification accuracy. The classification accuracy was reported using two different algorithms: Naive Bayes and K-nearest neighbor classifier.     

Content based image indexing and retrieval using directional local extrema and magnitude patterns

In this paper, we integrate the concept of directional local extremas and their magnitude based patterns for content based image indexing and retrieval. The standard ditectional local extrama pattern (DLEP) extracts the directional edge information based on local extrema in 0°, 45°, 90°, and 135° directions in an image. However, they are not considering the magnitudes of local extremas. The proposed method integrates these two concepts for better retrieval performance. The sign DLEP (SDLEP) operator is a generalized DLEP operator and magnitude DLEP (MDLEP) operator is calculated using magnitudes of local extremas. The performance of the proposed method is compared with DLEP, local binary patterns (LBPs), block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG) and local edge patterns for image retrieval (LEPINV) methods by conducting two experiments on benchmark databases, viz. Corel-5K and Corel-10K databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to other existing methods on respective databases.     

基于凹凸局部二值模式的纹理图像分类

针对传统局部二值模式(LBP)及其扩展方法往往会将具有不同视觉特征的局部邻域赋予相同二值模式值的问题,提出了一种新的凹-凸LBP划分方法。首先通过选择最优参数将具有相同二值模式值的邻域划分为凹凸两类,然后分别统计每类特征并组合在一起进行纹理分类。为验证新方法的性能,实验采用3个在纹理分析领域广泛应用的图像库进行分类实验,结果表明,本文方法明显提高了传统LBP方法的分辨能力。     

Texture classification and discrimination for region-based image retrieval

In RBIR, texture features are crucial in determining the class a region belongs to since they can overcome the limitations of color and shape features. Two robust approaches to model texture features are Gabor and curvelet features. Although both features are close to human visual perception, sufficient information needs to be extracted from their sub-bands for effective texture classification. Moreover, shape irregularity can be a problem since Gabor and curvelet transforms can only be applied on the regular shapes. In this paper, we propose an approach that uses both the Gabor wavelet and the curvelet transforms on the transferred regular shapes of the image regions. We also apply a fitting method to encode the sub-bands’ information in the polynomial coefficients to create a texture feature vector with the maximum power of discrimination. Experiments on texture classification task with ImageCLEF and Outex databases demonstrate the effectiveness of the proposed approach.