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介绍了一种基于色彩共生矩阵提取颜色-纹理特征的图像检索方法。在灰度共生矩阵方法的基础上,发展出色彩共生矩阵方法,解决了灰度共生矩阵方法不能有效处理真彩色图像的缺陷,并从色彩共生矩阵中提取颜色和纹理特征用于图像检索。该方法易于实现、特征库简洁高效,且具有较好的检索效果。 相似文献
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在基于火焰图像识别的转炉吹炼状态识别过程中,针对已有方法存在火焰彩色纹理信息利用不充分和状态识别率仍需提高的问题,提出一种基于火焰彩色纹理复杂度特征的转炉吹炼状态识别方法.首先,将火焰图像转化到HSI颜色空间下并作非均匀量化;然后,计算H分量和S分量的共生矩阵从而融入火焰图像的颜色信息;其次,利用得到的颜色共生矩阵计算火焰纹理复杂度的特征描述子;最后,应用Canberra距离作为相似度度量准则对吹炼状态进行分类和识别.实验结果表明,与已有的转炉火焰灰度共生矩阵和灰度差分统计方法相比,在满足吹炼识别实时性要求的前提下,所提方法的识别率分别提高了28.33%和3.33%. 相似文献
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针对单一固定特征无法适应目标及其背景变化的情况,提出了一种基于HSV颜色空间的共生矩阵的自适应特征选择的方法。首先将图像转换为HSV颜色空间,在每个颜色通道内对目标及其背景区域计算不同参数的共生矩阵,选择其中差异最大的前4个共生矩阵作为目标特征模型,通过比率对数矩阵计算的反向投影图,采样均值平移算法跟踪目标。仿真实验验证了该算法的有效性。 相似文献
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一种改进的颜色共生矩阵纹理描述符 总被引:1,自引:0,他引:1
利用模糊连续三角模运算定义的相似性度量描述彩色图像中像素点之间的差异程度,提出一种改进的颜色纹理特征描述符。该描述符首先将彩色图像多通道颜色信息按照间隔距离和方向进行有效融合并转换为伪灰度图像,然后再用灰度共生矩阵法提取图像的纹理特征矢量。在基于内容的图像检索测试平台上完成的实验表明,改进的纹理描述符所需特征矢量的维数与灰度共生矩阵描述符相同,而描述能力却能与各类颜色共生矩阵描述符相当,有效地实现了图像中纹理和颜色特征融合提取,提高了图像检索性能。 相似文献
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本文介绍了基于色彩共生矩阵的图像检索方法。在灰度共生矩阵的基础上,我们提出了色彩共生矩阵的概念,并从中提取出颜色和纹理特征进行图像检索。该算法易于实现,特征库简洁、高速、且具有较好的检索效果。 相似文献
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对于形状区别不大的目标,传统的矩不变量、傅里叶描述子等方法的描述能力受到一些限制.利用曲率信息来表达曲面的固有特征,引入纹理识别中共生矩阵的思想,将共生矩阵的定义从灰度延伸到曲率,构造出曲率共生矩阵.通过对曲率共生矩阵的归一化,设计了一类与平移、尺度和旋转变换无关的不变量.实验表明,基于曲率共生矩阵的三维目标识别方法,和传统方法相比,在不增加运算时间的情况下,能够更好地对形状丰富的目标进行分类. 相似文献
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基于分块颜色矩和灰度共生矩阵的图像检索 总被引:2,自引:0,他引:2
颜色和纹理是描述图像内容的两个重要视觉特征,提出了一种基于分块颜色矩和灰度共生矩阵相结合的图像检索方法。根据图像背景内容的差异,将图像分成大小相等的子块,通过HSV颜色空间非均匀量化,计算子块的颜色矩来描述图像的局部颜色特征。整体图像采用灰度共生矩阵作为其纹理特征。结合两者采用加权欧式距离计算图像相似度,从实验结果中得出该检索方法的有效性。 相似文献
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该文提出了一种用于墙地砖自动缺陷检测的新算法,该算法综合了颜色的空间分布信息和比例分布信息,应用共生矩阵纹理特征与颜色统计特征构造一个判断矢量,能对复杂纹理的多色墙地砖进行各种缺陷检测,实验证明该算法是有效的。 相似文献
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A novel binning and learning framework is presented for analyzing and applying large data sets that have no explicit knowledge of distribution parameterizations, and can only be assumed generated by the underlying probability density functions (PDFs) lying on a nonparametric statistical manifold. For models’ discretization, the uniform sampling-based data space partition is used to bin flat-distributed data sets, while the quantile-based binning is adopted for complex distributed data sets to reduce the number of under-smoothed bins in histograms on average. The compactified histogram embedding is designed so that the Fisher–Riemannian structured multinomial manifold is compatible to the intrinsic geometry of nonparametric statistical manifold, providing a computationally efficient model space for information distance calculation between binned distributions. In particular, without considering histogramming in optimal bin number, we utilize multiple random partitions on data space to embed the associated data sets onto a product multinomial manifold to integrate the complementary bin information with an information metric designed by factor geodesic distances, further alleviating the effect of over-smoothing problem. Using the equipped metric on the embedded submanifold, we improve classical manifold learning and dimension estimation algorithms in metric-adaptive versions to facilitate lower-dimensional Euclidean embedding. The effectiveness of our method is verified by visualization of data sets drawn from known manifolds, visualization and recognition on a subset of ALOI object database, and Gabor feature-based face recognition on the FERET database. 相似文献
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曾青松 《中国图象图形学报》2014,19(3):414-420
目的提出了黎曼流形上局部结构特征保持的图像集匹配方法。方法该方法使用协方差矩阵建模图像集合,利用对称正定的非奇异协方差矩阵构成黎曼流形上的子空间,将图像集的匹配转化为流形上的点的匹配问题。通过基于协方差矩阵度量学习的核函数将黎曼流形上的协方差矩阵映射到欧几里德空间。不同于其他方法黎曼流形上的鉴别分析方法,考虑到样本分布的局部几何结构,引入了黎曼流形上局部保持的图像集鉴别分析方法,保持样本分布的局部邻域结构的同时提升样本的可分性。结果在基于图像集合的对象识别任务上测试了本文算法,在ETH80和YouTube Celebrities数据库分别进行了对象识别和人脸识别实验,分别达到91.5%和65.31%的识别率。结论实验结果表明,该方法取得了优于其他图像集匹配算法的效果。 相似文献
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视频人脸识别的核心问题是如何准确、高效地构建人脸模型并度量模型的相似性,为此提出一种维数约减的格拉斯曼流形鉴别分析方法以提高集合匹配的性能。首先通过子空间建模图像集合,引入投影映射将格拉斯曼流形上的基本元素表示成对应的投影矩阵。然后,为解决高维矩阵计算开销大以及在小样本条件下不能有效描述样本分布的缺陷,引入二维主成分分析方法对子空间的正交基矩阵降维。通过QR分解正则化降维后的矩阵,得到一个低维、紧致的格拉斯曼流形以获得图像集更好的表达。最后将其投影到高维核空间中进行分类。在公开的视频数据库中的实验结果证明,提出的方法在降低计算开销的同时能够获得较高的正确率,是一种有效的基于集合的对象匹配和人脸识别方法。 相似文献
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Visual learning and recognition of 3-d objects from appearance 总被引:33,自引:9,他引:24
The problem of automatically learning object models for recognition and pose estimation is addressed. In contrast to the traditional approach, the recognition problem is formulated as one of matching appearance rather than shape. The appearance of an object in a two-dimensional image depends on its shape, reflectance properties, pose in the scene, and the illumination conditions. While shape and reflectance are intrinsic properties and constant for a rigid object, pose and illumination vary from scene to scene. A compact representation of object appearance is proposed that is parametrized by pose and illumination. For each object of interest, a large set of images is obtained by automatically varying pose and illumination. This image set is compressed to obtain a low-dimensional subspace, called the eigenspace, in which the object is represented as a manifold. Given an unknown input image, the recognition system projects the image to eigenspace. The object is recognized based on the manifold it lies on. The exact position of the projection on the manifold determines the object's pose in the image.A variety of experiments are conducted using objects with complex appearance characteristics. The performance of the recognition and pose estimation algorithms is studied using over a thousand input images of sample objects. Sensitivity of recognition to the number of eigenspace dimensions and the number of learning samples is analyzed. For the objects used, appearance representation in eigenspaces with less than 20 dimensions produces accurate recognition results with an average pose estimation error of about 1.0 degree. A near real-time recognition system with 20 complex objects in the database has been developed. The paper is concluded with a discussion on various issues related to the proposed learning and recognition methodology. 相似文献
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图像集匹配是当前图像处理和模式识别领域研究的热点问题之一。处理图像集合匹配一般将其映射到高维流形,然后在流形上度量2个点之间的距离。本文使用协方差矩阵对图像集合建模,把图像集合表达为黎曼流形上的一个点,将图像集的匹配问题转化为黎曼流形上的点的匹配问题,最后应用核鉴别分析方法进行分类。在基于图像集合的对象识别应用中测试本文所提出的算法,在公开数据库上的实验结果表明,本文所提出的方法在识别率上超越了当前主流的图像集匹配算法。 相似文献
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针对光照、表情变化给人脸识别造成的影响以及大型人脸图像库的训练样本中只有部分标记的问题,结合多通道Log-Gabor小波和半监督流形学习算法,提出一种新的人脸图像检索方法。该方法首先使用Log-Ga-bor小波对人脸图像进行滤波获得特征矩阵,进一步利用提出的二维半监督流形学习算法进行维数约简,得到低维判别特征。由于该方法直接作用于Log-Gabor特征矩阵,克服了小样本带来的奇异问题;另外,通过充分利用标记和未标记信息,还保留了数据的局部流形结构,增强了特征匹配的相似性。在CMU PIE和AR人脸数据库上的实验结果表明,该方法有效且优于其他方法。 相似文献
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Slater D. Healey G. 《IEEE transactions on pattern analysis and machine intelligence》1996,18(2):206-210
Traditional approaches to three dimensional object recognition exploit the relationship between three dimensional object geometry and two dimensional image geometry. The capability of object recognition systems can be improved by also incorporating information about the color of object surfaces. Using physical models for image formation, the authors derive invariants of local color pixel distributions that are independent of viewpoint and the configuration, intensity, and spectral content of the scene illumination. These invariants capture information about the distribution of spectral reflectance which is intrinsic to a surface and thereby provide substantial discriminatory power for identifying a wide range of surfaces including many textured surfaces. These invariants can be computed efficiently from color image regions without requiring any form of segmentation. The authors have implemented an object recognition system that indexes into a database of models using the invariants and that uses associated geometric information for hypothesis verification and pose estimation. The approach to recognition is based on the computation of local invariants and is therefore relatively insensitive to occlusion. The authors present several examples demonstrating the system's ability to recognize model objects in cluttered scenes independent of object configuration and scene illumination. The discriminatory power of the invariants has been demonstrated by the system's ability to process a large set of regions over complex scenes without generating false hypotheses 相似文献
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为了丰富训练样本的类内变化信息,提出了基于通用训练样本集的虚拟样本生成方法。进一步,为了利用生成的虚拟样本中的类内变化信息有效地完成单样本人脸识别任务,提出了基于虚拟样本图像集的多流行鉴别学习算法。该算法首先将每类仅有的单个训练样本图像和该类的虚拟样本图像划分为互补重叠的局部块并构建流形,然后为每个流形学习一个投影矩阵,使得相同流形内的局部块在投影后的低维特征空间间隔最小化,不同流形中的局部块在投影后的低维特征空间中间隔最大化。实验结果表明,所提算法能够准确地预测测试样本中的类内变化,是一种有效的单样本人脸识别算法。 相似文献