Edge-preserving-based collaborative representation for spectral-spatial classification

Recently, the nearest regularized subspace (NRS) classifier and its spectral–spatial versions such as joint collaborative representation (JCR) and weighted JCR (WJCR) have gained an increasing interest in the hyperspectral image classification. JCR and WJCR average each pixel with its neighbours in a spatial neighbourhood window. The use of spatial information as averaging of pixels in a local window may degrade the classification accuracy in the neighbourhood of discontinuities and class boundaries. We propose the edge-preserving-based collaborative representation (EPCR) classifier in this article, which overcomes this problem by using the edge image estimated by the original full-band hyperspectral image. The estimated edge image is used for calculation of the weights of neighbours and also the final residuals in the collaborative representation classifier. The advantage of multiscale spatial window is also assessed in this work. Moreover, the kernelized versions of NRS and its improved versions are developed in this article. Our experimental results on several popular hyperspectral images indicate that EPCR and its kernelized version are superior to some state-of-the-art classification methods.     

基于改进局部特征分布的图像分类方法

提出一种概率签名的图像分布描述及对应的图像分类算法．算法首先通过高斯混合模型建立图像局部特征分布,然后以混合模型中各个模式的均值为聚类中心,以图像中满足约束条件的局部特征对相应模式的后验概率之和为聚类大小来形成初始的概率签名,最后执行一个压缩过程确定最终的概率签名特征,并通过训练基于Earth Mover's Distance (EMD)核的SVM分类器完成图像分类．概率签名允许一个局部特征对多个聚类做出反映,可以编码更多判别信息以及从视觉感知上捕捉更多的相似性．通过与其它图像分类方法在场景识别和对象分类两项任务上的对比实验,验证了文中提出的分类方法的有效性．     

一种结合显著性检测与词袋模型的目标识别方法

针对词袋模型易受到无关的背景视觉噪音干扰的问题,提出了一种结合显著性检测与词袋模型的目标识别方法。首先,联合基于图论的视觉显著性算法与一种全分辨率视觉显著性算法,自适应地从原始图像中获取感兴趣区域。两种视觉显著性算法的联合可以提高获取的前景目标的完整性。然后,使用尺度不变特征变换描述子从感兴趣区域中提取特征向量,并通过密度峰值聚类算法对特征向量进行聚类,生成视觉字典直方图。最后,利用支持向量机对目标进行识别。在PASCAL VOC 2007和MSRC-21数据库上的实验结果表明,该方法相比同类方法可以有效地提高目标识别性能。     

Improving hyperspectral image classification by combining spectral,texture, and shape features

Several studies have already demonstrated the efficiency of utilizing spatial information in representation and interpretation of hyperspectral (HS) images. Texture and shape features are known as two important categories of spatial information in various applications of image processing. This study tries to utilize texture and shape features extracted from HS images, as well as spectral information, in order to reduce overall classification errors. These features include morphological profiles (MPs), global Gabor features, and features extracted from conventional and segmentation-based grey-level co-occurrence matrices (GLCMs). Various combinations of these spatial features along with spectral information are fed into a support vector machine (SVM) classifier, and the best combinations for different situations are determined. Experiments on the widely used Indian Pines, Pavia University, and Salinas HS data sets demonstrate the efficiency of the proposed framework in comparison with some recent spectral–spatial classification methods.     

用于遥感图像建筑物目标分类的层次匹配核

提出了一种利用图像特征空间信息的核函数——层次对数极坐标匹配核,用于遥感图像建筑物目标的分类。对图像进行特征提取,并将特征映射到已聚类好的"码本"中,量化为有限个类别。将图像由粗到细划分为多个层次的对数极坐标系下的"子区域(单元格)"。比对落入同一层次、同一"子区域(单元格)"的每类特征的直方图交集,建立加权的多尺度直方图,将多个特征多尺度直方图合并,得到最终的核函数,并利用"一对多"的支持向量机(supportvector machine,SVM)完成建筑物的分类。对标准数据库Caltech-256和自建遥感图像数据集进行实验,结果证明了该核函数的有效性。     

Adaptable image segmentation via simple pixel classification

We propose an approach to image segmentation that views it as one of pixel classification using simple features defined over the local neighborhood. We use a support vector machine for pixel classification, making the approach automatically adaptable to a large number of image segmentation applications. Since our approach utilizes only local information for classification, both training and application of the image segmentor can be done on a distributed computing platform. This makes our approach scalable to larger images than the ones tested. This article describes the methodology in detail and tests it efficacy against 5 other comparable segmentation methods on 2 well‐known image segmentation databases. Hence, we present the results together with the analysis that support the following conclusions: (i) the approach is as effective, and often better than its studied competitors; (ii) the approach suffers from very little overfitting and hence generalizes well to unseen images; (iii) the trained image segmentation program can be run on a distributed computing environment, resulting in linear scalability characteristics. The overall message of this paper is that using a strong classifier with simple pixel‐centered features gives as good or better segmentation results than some sophisticated competitors and does so in a computationally scalable fashion.     

Combining spatial and colour information for content based image retrieval

Colour is one of the most important features in content based image retrieval. However, colour is rarely used as a feature that codes local spatial information, except for colour texture. This paper presents an approach to represent spatial colour distributions using local principal component analysis (PCA). The representation is based on image windows which are selected by two complementary data driven attentive mechanisms: a symmetry based saliency map and an edge and corner detector. The eigenvectors obtained from local PCA of the selected windows form colour patterns that capture both low and high spatial frequencies, so they are well suited for shape as well as texture representation. Projections of the windows selected from the image database to the local PCs serve as a compact representation for the search database. Queries are formulated by specifying windows within query images. System feedback makes both the search process and the results comprehensible for the user.     

Band selection and evaluation with spatial information

Spatial information has been widely used for hyperspectral image classification, which can dramatically improve the classification accuracy. Though band selection is an important pre-processing step for hyperspectral image processing, spatial information has not been well exploited in this field. In this article, we will exploit the spatial information for band selection. This article mainly includes two parts: algorithm design, and algorithm evaluation. In the first part, we propose an efficient band selection method by using the spatial structure information and spectral information. In the second part, we advocate the use of the local spatial filtering and the spectral-spatial classifier for evaluating the performance of band selection algorithms instead of the traditional pixel-wise classifiers. Comprehensive experiments over diverse publicly available benchmark data sets reveal some interesting results.     

多特征融合的遥感图像分类

针对高分辨率遥感图像特点,提出了一种多特征融合的分类方法。该方法首先改进了原始的视觉词袋生成算法;然后,分别提取图像的视觉词袋局部特征、颜色直方图特征以及Gabor纹理特征;最后采用支持向量机进行分类,并对多特征分类结果进行自适应综合。采用一个具有2 100幅图像的大型遥感图像分类公共测试数据集进行分类实验,与仅用单一特征分类方法的最高分类精度相比,本文多特征融合的遥感影像分类方法总体平均分类精度提高了10%,表明本文提出方法是一种有效的高分辨率遥感图像分类方法     

户外天气状况分类识别

针对户外监控系统需要利用图像画面进行天气状态识别的问题,提出了一种新的词袋模型,以及SVM和随机森林相结合的分类方法,对晴天与阴天两类天气状态进行识别.词袋模型利用SIFT特征,通过聚类构建词典,并用最小二乘法求解最佳图像的词典结构参数,最终根据金字塔匹配得到多尺度图像词袋模型特征.分类器的构造采用支持向量机（SVM）作为一级分类器,对小置信样本进行粗分类,之后,再利用随机森林构造作为二级分类器进行判别.通过对两类天气图像集的10 000张图像进行测试,其识别准确率验证了方法的有效性.     

基于SVM的局部潜在语义分析算法研究

针对现有的Web文本分类与表示方法中出现的各种分类效果与性能优化等问题,基于局部潜在语义分析的理论原理,利用支持向量机分类优势,设计出一种基于文档与类别之间相关度的生成局部区域的算法,即S-LLSA。该算法在语义分析使用矩阵的奇异值分解过程中引入不同类别信息,分析特征词的局部特征,使用支持向量机分类器计算文本对类别的相关度参数,并应用于局部区域生成过程。通过实验表明,S-LLSA算法有效解决了局部区域如何进行局部奇异值分解问题,有效提高并优化了Web文本分类效果,更好地表示了Web文本潜在语义空间。     

边缘保持滤波的遥感影像多特征联合分类

针对高分辨率影像在分类时存在的"海量数据灾难""椒盐"现象、地物边缘不可分性强的问题,提出边缘保持滤波的高分辨率遥感影像多特征联合分类方法.该方法主要分为3部分.首先,提取影像的多种特征进行联合处理,减少数据处理运算量;然后,利用极限学习机对样本子类训练多个弱分类器,通过分类器获得初始类别概率影像;最后,利用多特征联合...     

Semi-supervised object recognition based on Connected Image Transformations

We present a novel semi-supervised classifier model based on paths between unlabeled and labeled data through a sequence of local pattern transformations. A reliable measure of path-length is proposed that combines a local dissimilarity measure between consecutive patters along a path with a global, connectivity-based metric. We apply this model to problems of object recognition, for which we propose a practical classification algorithm based on sequences of “Connected Image Transformations” (CIT). Experimental results on four popular image benchmarks demonstrate how the proposed CIT classifier outperforms state-of-the-art semi-supervised techniques. The results are particularly significant when only a very small number of labeled patterns is available: the proposed algorithm obtains a generalization error of 4.57% on the MNIST data set trained on 2000 randomly chosen patterns with only 10 labeled patterns per digit class.     

融合极端学习机的判别性分析字典学习算法

研究表明,端学习机和判别性字典学习算法在图像分类领域极具有高效和准确的优势。然而,这两种方法也具有各自的缺点,极端学习机对噪声的鲁棒性较差,判别性字典学习算法在分类过程中耗时较长。为统一这种互补性以提高分类性能,文中提出了一种融合极端学习机的判别性分析字典学习模型。该模型利用迭代优化算法学习最优的判别性分析字典和极端学习机分类器。为验证所提算法的有效性,利用人脸数据集进行分类。实验结果表明,与目前较为流行的字典学习算法和极端学习机相比,所提算法在分类过程中具有更好的效果。     

基于词包和特征融合的目标识别算法

针对现有词包模型对目标识别性能的不足,对特征提取、图像表示等方面进行改进以提高目标识别的准确率。首先,以密集提取关键点的方式取代SIFT关键点提取,减少了计算时间并最大程度地描述了图像底层信息。然后采用尺度不变特征变换(Scale-invariant feature transform, SIFT)描述符和统一模式的局部二值模式(Local binary pattern,LBP)描述符描述关键点周围的形状特征和纹理特征,引入K-Means聚类算法分别生成视觉词典,然后将局部描述符进行近似局部约束线性编码,并进行最大值特征汇聚。分别采用空间金字塔匹配生成具有空间信息的直方图,最后将金字塔直方图相串联,形成特征的图像级融合,并送入SVM进行分类识别。在公共数据库中进行实验,实验结果表明,本文所提方法能取得较高的目标识别准确率。     

GPU-based fast hyperspectral image classification using joint sparse representation with spectral consistency constraint

Due to the fact that neighboring hyperspectral pixels often belong to the same class with high probability, spatial correlation between pixels has been widely used in hyperspectral image classification. In this paper, a novel joint sparse representation classifier with spectral consistency constraint (JSRC-SCC) is proposed. Specifically, to efficiently exploit contextual structure information, a local adaptive weighted average value is reallocated as the central pixel of a window through spatial filtering, and then, representation coefficients are estimated by the joint sparse representation model, which is imposed by the spectral consistency constraint under \(\textit{l}_1\)-minimization. For the purpose of fast classification, graphics processing units are adopted to accelerate this model. Experimental results on two classical hyperspectral image data sets demonstrate the proposed method can not only produce satisfying classification performance, but also shorten the computational time significantly.     

Statistical Image Object Recognition using Mixture Densities

In this paper, we present a mixture density based approach to invariant image object recognition. To allow for a reliable estimation of the mixture parameters, the dimensionality of the feature space is optionally reduced by applying a robust variant of linear discriminant analysis. Invariance to affine transformations is achieved by incorporating invariant distance measures such as tangent distance. We propose an approach to estimating covariance matrices with respect to image variabilities as well as a new approach to combined classification, called the virtual test sample method. Application of the proposed classifier to the well known US Postal Service handwritten digits recognition task (USPS) yields an excellent error rate of 2.2%. We also propose a simple, but effective approach to compensate for local image transformations, which significantly increases the performance of tangent distance on a database of 1,617 medical radiographs taken from clinical daily routine.