基于图像纹理特征提取算法的研究及应用
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摘要
纹理是图像的基本特征,是物体表面固有属性的描述,为图像分析提供了重要的视觉线索。纹理分类是图像处理、计算机视觉和模式识别领域重要的研究课题；在过去的几十年间吸引了科研人员广泛的研究兴趣和注意。纹理分类在遥感分析,工业纺织品检测,医学图像分析,场景和目标识别,基于内容的图像和视频分析以及材料分类等也有着广泛的潜在应用。纹理图像分类,尤其是对真实世界纹理分类的一个最大挑战在于诸如光照变化、旋转变化、空间尺度变化、对比度变化、视点变化、非刚体形变以及遮挡等都会引起纹理外观在视觉上的随机性突变、几何变化和光度变换等。因此,本论文主要研究如何从纹理图像中提取有效的纹理特征,使类内和类间的纹理属性具有高度可区分性,即构建的图像纹理特征能使同一类纹理图像间的相似度最大,而使不同类之间纹理图像的相似度最小。本论文的主要研究内容如下。
针对局部二元模式(LBP, Local binary pattern)纹理特征描述子存在的对噪声敏感、对纹理的宏模式描述不足以及特征维度随邻域数目的增加而不断增大等问题,提出-种模拟视网膜采样模式并结合图像像素和图像子块的采样结构(PTP, Pixel to patch)和一种邻域强度关系算子(NIR, neighboring intensity releationship)。该PTP结构可以同时捕获图像的微模式和宏模式。利用NIR算子构建了局部邻域强度关系模式(LNIRP, Local neighboring intensity releationship pattern)特征描述子来搜索邻域灰度属性信息,该特征描述子对LBP特征具有互补性。基于PTP和NIR提出了一种新的纹理描述方法。该方法首先采用联合统计将LNIRP特征与LBP特征相融合。其次,将融合后的特征采用PTP采样结构进一步扩展表达纹理的描述。理论分析及纹理分类实验结果表明,同其他纹理分类方法相比,本文提出的方法具有抗噪声鲁棒性,特征维度低,计算效率和分类正确率高等优点。
针对灰度不变和旋转不变的纹理分类问题,提出了两个新的基于局部二元模式的特征算子——环方向导数算子(CDD, Circum-Directional derivative)和环方向模式算子(CDP, Circum-Directional pattern),基于这两个特征算子提出了一种新的纹理分类方法。该方法首先利用CDD算子搜索环方向的导数信息,利用CDP算子来捕获中心像素信息及中心像素与环向邻域像素间的空间结构(模式)信息。其次,将CDD算子和CDP算子扩展为高阶形式及不同的变种来分别编码局部区域内的可分辨信息。第三,采用类似于LBP特征的构建方法,基于CDD和CDP算子分别构建局部二元环方向导数(LB-CDD, Local binary circum-directional derivative)和局部二元环方向模式(LB-CDP, Local binary circum-directional pattern)特征描述子。最后,将提出的LB-CDD和LB-CDP特征描述子采用联合统计的方法进行特征融合,根据LB-CDD的不同阶数和LB-CDP的不同变种,构建了多个融合特征描述子。在几个具有挑战性的旋转不变纹理分类数据库上的实验结果表明,提出的方法与其他方法相比在分类正确率上取得了显著的改进,且构建特征的维度较小。
针对Gabor变换的人脸表情描述方法计算代价和存储空间开销较高的问题,提出一种基于PTP采样结构和空间显著性相结合的人脸表情描述方法。该方法首先采用单演信号分析将人脸图像分解为单演幅度、相位和方向三个单演特征响应图。其次,将每个特征响应图划分为多个矩形子区域,采用矩形子区域上的单演幅度值作为该矩形子区域的空间显著性并为每个子区域分配不同的权重。然后,在三个特征响应图的每个子区域上分别提取基于PTP采样结构的单演幅度、相位和方向二元模式特征。最后,将结合了空间显著性的三个加权特征进行拼接融合进一步增强特征的可分辨性。在人脸表情数据库上的实验结果表明,提出的方法具有较高的准确率和较低的特征维度,是一种有效的人脸表情识别方法。
针对铁路安全运输中铁道线路上扣件系统检测存在的低效率和准确率不高的问题,基于PTP采样结构和视觉跟踪技术,提出一种扣件定位和扣件状态实时检测方法。首先,利用轨枕区域对光照反射的平均灰度强度和区域内灰度的投影残差实现扣件区域的粗定位,将定位到的轨枕区域设置为扣件区域的粗定位坐标。其次,利用基于PTP采样结构的LBP描述子在扣件区域周围提取正、负样本特征。然后,利用具有在线学习和自动更新的朴素贝叶斯分类器跟踪扣件区域的精确位置。最后,提取扣件区域精确位置上的特征并对特征分类,实现扣件状态的检测。在铁路视频数据库上的实验结果表明,该方法能实时跟踪铁路图像中扣件区域的精确位置,并能自动检测扣件的状态。提出的算法具有较高的检测正确率和实时跟踪速度,是一种有效的和鲁棒的检测方法。
最后,分析了相关的理论及方法,总结了本文提出的纹理特征的提取方法及在人脸表情识别和轨道扣件状态检测中的应用。针对本文存在的不足之处讨论和分析了未来进一步的研究工作。
Texture, one of basic attributes of the image and the descriptions of intrinsic property for object surface, provides important visual cues for image analysis. Texture classification is an important topic in the fields of image processing, computer vision and pattern recognition, and has received a lot of research interest and attention during the past decades. There are a wide variety of potential applications, such as remote sensing analysis, industrial fabrics inspection, medical image analysis, scene and object recognition, content-based image and video analysis, and material classification. However, the major challenge for texture classification, especially for analyzing real-world textures, lies in the large variety of geometric, stochastic and photometric transformations on the appearance of textures, which is caused by illumination changes, rotation variations, variability in scale and contrast, viewpoint changes, non-rigid deformations and occlusions. Therefore, this paper focus on how to extract effective texture features which are able to highly distinguish between intra-class and inter-class attributes of texture. It means that the constructed texture descriptors can maximize the similarity of inter-class while minimizing the ones of intra-class. The main contents of this paper are as follows.
For the problem of sensitiveness to noise, high dimensionality and inadequate description of macro-mode existing in the Local binary pattern (LBP) descriptor, an effective sampling structure based on Pixel To Patch (PTP) to mimic the retinal sampling pattern and a novel local neighboring intensity relationship pattern (LNIRP) descriptor are proposed. The proposed PTP sampling structure simultaneously captures micro-patterns and macro-patterns. LNIRP descriptor, which is complementary to the LBP descriptor, is built by using neighboring intensity relationship (NIR) operator to explore neighboring gray-scale properties. A new texture description method is proposed based on PTP and NIR. With this method, LNIRP and LBP features are firstly fused jointly. Then, the joint descriptors are extended by using PTP sampling structure to describe textures. Theoretical analysis and texture classification experimental results show that the proposed descriptor has advantages of robustness to noise, low feature dimensionality, higher computational efficiency and classification accuracy. Two novel operators, circum-directional derivative (CDD) and circum-directional pattern (CDP)--are proposed for gray-scale and rotation invariant texture classification. And a new texture description method is proposed based on the above two operators. Firstly, it adopts CDD operator to explore circum-directional derivative information, and CDP operator to capture the central pixel information and spatial structure (pattern) information between central pixel and circum-directional neighboring pixels. Secondly, CDD and CDP operators are further extended to higher orders and different variants to encode more discriminative information in a given local region respectively. Thirdly, in a way similar to LBP, these two operators are then adopted to build two new texture descriptors respectively, namely CDD for the local binary circum-directional derivative descriptor (LB-CDD) and CDP for the local binary circum-directional pattern descriptor (LB-CDP). Finally, the proposed LB-CDD and LB-CDP descriptors are fused jointly. According to the different orders of LB-CDD and different variants of LB-CDP, several fused descriptors are built. Based on several challenging rotation invariant texture classification databases, experimental results show that the proposed method significantly outperformed other methods in classification accuracy as well as keeping a smaller feature dimension.
For the problem of high expenses for computation and storage space of Gabor transformation based facial expression description, a combination of PTP based sampling structure and spatial saliency based method is proposed for the description of facial expression. It firstly adopts monogenic signal analysis to decompose a facial image into three feature maps for monogenic amplitude, phase and orientation. Secondly, each feature map is divided into multiple rectangular sub-regions. The amplitudes of the rectangular sub-region are set as the spatial saliency which is allocated to each rectangle as different weights. Then, on the each rectangular sub-region, LBP feature based on PTP are respectively extracted from three monogenic maps. Finally, the combination of those features, which are weighted by spatial saliency, are concatenated together to further enhance the discrimination. Experimental results on facial expression databases show that the proposed method which has higher accuracy and lower feature dimension, is an effective method of facial expression recognition.
For the problem of low efficiency and accuracy for fastening systems inspection in safe rail transportation, a method on fastener detection and status inspection is proposed based on PTP sampling structure and visual tracking technology. Firstly, coarse positions of fasteners can be detected through adopting local region's average intensity and projection residuals of gray-values. The positions of detected sleepers are set as coordinates of fasteners. Based on PTP, LBP features are extracted from positive and negative samples which are sampled around the fasteners. The exact locations of fasteners are tracked via naive Bayes classifier capable of online learning and automatic updating. Finally, the features extracted from exact location of fasteners are classified to detect the status of the fasteners.
Adopting the railway video databases, it experimentally showed that the proposed approach can effectively track the fasteners and automatic inspect their status. The proposed method which has higher recognition accuracy and real-time tracking speed, is an effective and robust inspection approach.
Finally, after analyzing the relevant theories and methods, we conclude the proposed multi-scale texture feature extraction method and its application in face expression recognition and railway fastener detection. For the shortcoming of the presented method, we discuss and analyze the further research work in the future.
针对局部二元模式(LBP, Local binary pattern)纹理特征描述子存在的对噪声敏感、对纹理的宏模式描述不足以及特征维度随邻域数目的增加而不断增大等问题,提出-种模拟视网膜采样模式并结合图像像素和图像子块的采样结构(PTP, Pixel to patch)和一种邻域强度关系算子(NIR, neighboring intensity releationship)。该PTP结构可以同时捕获图像的微模式和宏模式。利用NIR算子构建了局部邻域强度关系模式(LNIRP, Local neighboring intensity releationship pattern)特征描述子来搜索邻域灰度属性信息,该特征描述子对LBP特征具有互补性。基于PTP和NIR提出了一种新的纹理描述方法。该方法首先采用联合统计将LNIRP特征与LBP特征相融合。其次,将融合后的特征采用PTP采样结构进一步扩展表达纹理的描述。理论分析及纹理分类实验结果表明,同其他纹理分类方法相比,本文提出的方法具有抗噪声鲁棒性,特征维度低,计算效率和分类正确率高等优点。
针对灰度不变和旋转不变的纹理分类问题,提出了两个新的基于局部二元模式的特征算子——环方向导数算子(CDD, Circum-Directional derivative)和环方向模式算子(CDP, Circum-Directional pattern),基于这两个特征算子提出了一种新的纹理分类方法。该方法首先利用CDD算子搜索环方向的导数信息,利用CDP算子来捕获中心像素信息及中心像素与环向邻域像素间的空间结构(模式)信息。其次,将CDD算子和CDP算子扩展为高阶形式及不同的变种来分别编码局部区域内的可分辨信息。第三,采用类似于LBP特征的构建方法,基于CDD和CDP算子分别构建局部二元环方向导数(LB-CDD, Local binary circum-directional derivative)和局部二元环方向模式(LB-CDP, Local binary circum-directional pattern)特征描述子。最后,将提出的LB-CDD和LB-CDP特征描述子采用联合统计的方法进行特征融合,根据LB-CDD的不同阶数和LB-CDP的不同变种,构建了多个融合特征描述子。在几个具有挑战性的旋转不变纹理分类数据库上的实验结果表明,提出的方法与其他方法相比在分类正确率上取得了显著的改进,且构建特征的维度较小。
针对Gabor变换的人脸表情描述方法计算代价和存储空间开销较高的问题,提出一种基于PTP采样结构和空间显著性相结合的人脸表情描述方法。该方法首先采用单演信号分析将人脸图像分解为单演幅度、相位和方向三个单演特征响应图。其次,将每个特征响应图划分为多个矩形子区域,采用矩形子区域上的单演幅度值作为该矩形子区域的空间显著性并为每个子区域分配不同的权重。然后,在三个特征响应图的每个子区域上分别提取基于PTP采样结构的单演幅度、相位和方向二元模式特征。最后,将结合了空间显著性的三个加权特征进行拼接融合进一步增强特征的可分辨性。在人脸表情数据库上的实验结果表明,提出的方法具有较高的准确率和较低的特征维度,是一种有效的人脸表情识别方法。
针对铁路安全运输中铁道线路上扣件系统检测存在的低效率和准确率不高的问题,基于PTP采样结构和视觉跟踪技术,提出一种扣件定位和扣件状态实时检测方法。首先,利用轨枕区域对光照反射的平均灰度强度和区域内灰度的投影残差实现扣件区域的粗定位,将定位到的轨枕区域设置为扣件区域的粗定位坐标。其次,利用基于PTP采样结构的LBP描述子在扣件区域周围提取正、负样本特征。然后,利用具有在线学习和自动更新的朴素贝叶斯分类器跟踪扣件区域的精确位置。最后,提取扣件区域精确位置上的特征并对特征分类,实现扣件状态的检测。在铁路视频数据库上的实验结果表明,该方法能实时跟踪铁路图像中扣件区域的精确位置,并能自动检测扣件的状态。提出的算法具有较高的检测正确率和实时跟踪速度,是一种有效的和鲁棒的检测方法。
最后,分析了相关的理论及方法,总结了本文提出的纹理特征的提取方法及在人脸表情识别和轨道扣件状态检测中的应用。针对本文存在的不足之处讨论和分析了未来进一步的研究工作。
Texture, one of basic attributes of the image and the descriptions of intrinsic property for object surface, provides important visual cues for image analysis. Texture classification is an important topic in the fields of image processing, computer vision and pattern recognition, and has received a lot of research interest and attention during the past decades. There are a wide variety of potential applications, such as remote sensing analysis, industrial fabrics inspection, medical image analysis, scene and object recognition, content-based image and video analysis, and material classification. However, the major challenge for texture classification, especially for analyzing real-world textures, lies in the large variety of geometric, stochastic and photometric transformations on the appearance of textures, which is caused by illumination changes, rotation variations, variability in scale and contrast, viewpoint changes, non-rigid deformations and occlusions. Therefore, this paper focus on how to extract effective texture features which are able to highly distinguish between intra-class and inter-class attributes of texture. It means that the constructed texture descriptors can maximize the similarity of inter-class while minimizing the ones of intra-class. The main contents of this paper are as follows.
For the problem of sensitiveness to noise, high dimensionality and inadequate description of macro-mode existing in the Local binary pattern (LBP) descriptor, an effective sampling structure based on Pixel To Patch (PTP) to mimic the retinal sampling pattern and a novel local neighboring intensity relationship pattern (LNIRP) descriptor are proposed. The proposed PTP sampling structure simultaneously captures micro-patterns and macro-patterns. LNIRP descriptor, which is complementary to the LBP descriptor, is built by using neighboring intensity relationship (NIR) operator to explore neighboring gray-scale properties. A new texture description method is proposed based on PTP and NIR. With this method, LNIRP and LBP features are firstly fused jointly. Then, the joint descriptors are extended by using PTP sampling structure to describe textures. Theoretical analysis and texture classification experimental results show that the proposed descriptor has advantages of robustness to noise, low feature dimensionality, higher computational efficiency and classification accuracy. Two novel operators, circum-directional derivative (CDD) and circum-directional pattern (CDP)--are proposed for gray-scale and rotation invariant texture classification. And a new texture description method is proposed based on the above two operators. Firstly, it adopts CDD operator to explore circum-directional derivative information, and CDP operator to capture the central pixel information and spatial structure (pattern) information between central pixel and circum-directional neighboring pixels. Secondly, CDD and CDP operators are further extended to higher orders and different variants to encode more discriminative information in a given local region respectively. Thirdly, in a way similar to LBP, these two operators are then adopted to build two new texture descriptors respectively, namely CDD for the local binary circum-directional derivative descriptor (LB-CDD) and CDP for the local binary circum-directional pattern descriptor (LB-CDP). Finally, the proposed LB-CDD and LB-CDP descriptors are fused jointly. According to the different orders of LB-CDD and different variants of LB-CDP, several fused descriptors are built. Based on several challenging rotation invariant texture classification databases, experimental results show that the proposed method significantly outperformed other methods in classification accuracy as well as keeping a smaller feature dimension.
For the problem of high expenses for computation and storage space of Gabor transformation based facial expression description, a combination of PTP based sampling structure and spatial saliency based method is proposed for the description of facial expression. It firstly adopts monogenic signal analysis to decompose a facial image into three feature maps for monogenic amplitude, phase and orientation. Secondly, each feature map is divided into multiple rectangular sub-regions. The amplitudes of the rectangular sub-region are set as the spatial saliency which is allocated to each rectangle as different weights. Then, on the each rectangular sub-region, LBP feature based on PTP are respectively extracted from three monogenic maps. Finally, the combination of those features, which are weighted by spatial saliency, are concatenated together to further enhance the discrimination. Experimental results on facial expression databases show that the proposed method which has higher accuracy and lower feature dimension, is an effective method of facial expression recognition.
For the problem of low efficiency and accuracy for fastening systems inspection in safe rail transportation, a method on fastener detection and status inspection is proposed based on PTP sampling structure and visual tracking technology. Firstly, coarse positions of fasteners can be detected through adopting local region's average intensity and projection residuals of gray-values. The positions of detected sleepers are set as coordinates of fasteners. Based on PTP, LBP features are extracted from positive and negative samples which are sampled around the fasteners. The exact locations of fasteners are tracked via naive Bayes classifier capable of online learning and automatic updating. Finally, the features extracted from exact location of fasteners are classified to detect the status of the fasteners.
Adopting the railway video databases, it experimentally showed that the proposed approach can effectively track the fasteners and automatic inspect their status. The proposed method which has higher recognition accuracy and real-time tracking speed, is an effective and robust inspection approach.
Finally, after analyzing the relevant theories and methods, we conclude the proposed multi-scale texture feature extraction method and its application in face expression recognition and railway fastener detection. For the shortcoming of the presented method, we discuss and analyze the further research work in the future.
引文
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