基于HSV模型和特征点匹配的行人重识别算法

提出了一种基于HSV模型和特征点匹 配相结合的行 人重识别算法。首先根据改进的HSV空间颜色量化策略,比对两幅行人图像的 躯干和腿部主颜色是否一致,以快速确定备选目标;然后对备选目标,利用环形Gabor滤波器组生成多尺度图 像,再利用改进的FAST算法和BRIEF算法对多尺度图像进行特征点提取与描述,最后利用暴 力算法和随机抽样一 致性算法进行特征点匹配和提纯,以达到较好的匹配效果。实验结果表明,本文提出的行人 重识别算法具有较高的识别准确率,识别速度达到12frames。     

一种改进的颜色匹配对图像检索算法

以子块直方图彩色图像检索算法为基础， 分析了进一步利用图像空间相似信息的颜色匹配对检索算法的性能。在子块直方图的构成、直方图距离值的归类等方面提出了行之有效的改进方法；给出了子块大小、相似度阈值等参数选择的优化原则，使查准率、查全率等检索性能指标得到了较大的提高，得出了几个有用的结论并形成了实验系统。     

基于颜色和空间特征的彩色图像获取方法

本文针对基于内容的彩色图像相似性获取问题,提出了一种新的相似性获取方法,它既考虑了颜色内容特征,也包含了图像的空间关系特征.文章设计了无序矩阵来描述图像空间位置的相对关系,这种无序矩阵具有对旋转和平移不变性的特点.在进行图像间相似性测量时,首先用颜色直方图相交法对图像全局颜色分布进行相似性测量,再对得到的候选图像队列用新的算法进行测量,剔除不相似的图像.新的测量算法既考虑了颜色内容的匹配,同时也考虑了空间相似性的因素.用不同类型的图像对这种方法进行实验,其结果表明,它在彩色图像相似性检索时是很有效的,具有较高的检索精度.此外,它在图像发生小的旋转和平移等形变时,表现出较高的可靠性.     

Massive-scale image retrieval based on deep visual feature representation

This paper proposes an image retrieval algorithm towards massive-scale multimedia data. In order to be consistent with human visual system, we first design a color attention function to describe the important of different image patches. Subsequently, we combine color and texture to construct candidate regions, which will be fed into a deep neural network (DNN) for deep representation extraction. Then, we design a similarity function to calculate the distance among different images, where top-ranking images are considered as the required images. Experimental results show the effectiveness and robustness of our proposed method.     

Region-based image retrieval using color-size features of watershed regions

This paper presents a region-based image retrieval system that provides a user interface for helping to specify the watershed regions of interest within a query image. We first propose a new type of visual features, called color-size feature, which includes color-size histogram and moments, to integrate color and region-size information of watershed regions. Next, we design a scheme of region filtering that is based on color-size histogram to fast screen out some of most irrelevant regions and images for the preprocessing of the image retrieval. Our region-based image retrieval system applies the Earth Mover’s Distance in the design of the similarity measure for image ranking and matching. Finally, we present some experiments for the color-size feature, region filtering, and retrieval results that demonstrate the efficiency of our proposed system.     

基于分块颜色特征和相关反馈的图像检索技术

在基于内容的图像检索(CBIR)中,对于传统的颜色直方图完全没有空间分布信息,提出了一种新的分块划分,并且结合颜色特征的图像检索方法。该方法结合了图像的整体与分块颜色分布,两幅图像之间的相似度为整体相似度和分块局部相似度的加权和,并且在检索中加入相关反馈技术,针对检索结果适当地调整权值,以达到更新图像整体与局部颜色特征的权重的目的。最后,实验结果表明该算法能很好地提高检索性能。     

基于二维压缩感知和分层特征的图像检索算法

为了保留图像分析时的像素点位置关系及降维处理,把一维压缩感知理论推广到二维,建立了二维可稀疏信号的压缩测量模型,研究了一种二维信号的自适应梯度下降重构AGDR(Adaptive Gradient Descent Recursion)算法,由此提出了一种图像分层特征提取与检索方法.首先对图像在RGB颜色空间上进行网格离散划分,通过分层算子对图像进行分层映射,定义一种基于颜色网格空间的扩展灰度共生矩阵,采用二维测量模型获取图像的分层测量特征、纹理特征与分层颜色统计特征,图像分层测量特征综合反映出图像的颜色及像素点位置的关系,扩展灰度共生矩阵反映纹理特征.其次用AGDR算法计算检索图像之间的原始信号差量及其稀疏值.最后结合两类分层特征差量、稀疏值和颜色统计特征,融合计算图像间整体相似度度量指标.仿真实验表明,应用分层二维压缩感知测量与AGDR算法的图像检索方法在检索时间、查全率和查准率等指标上具有优越性能,为图像检索提供了新思路.     

基于小波模糊聚类区域分割的图像检索

基于内容的图像检索是近年来的研究热点,本文给出一种基于区域分割的图像检索算法。算法首先对图像按JPEG2000标准进行小波变换,对变换得到的低频子带提取一定的颜色和纹理特征用于模糊聚类,从而将图像的低频子带分割为一定的区域,将分割结果映射回整幅图像,提取整幅图像各个区域的特征矢量,用于区域相似度比较,最后按照一定的区域匹配准则得到整幅图像之间的相似度。实验结果表明,本文算法具有良好的图像检索性能。     

Image retrieval using both color and texture features

In order to improve the retrieval performance of images, this paper proposes an efficient approach for extracting and retrieving color images. The block diagram of our proposed approach to content-based image retrieval (CBIR) is given firstly, and then we introduce three image feature extracting arithmetic including color histogram, edge histogram and edge direction histogram, the histogram Euclidean distance, cosine distance and histogram intersection are used to measure the image level similarity. On the basis of using color and texture features separately, a new method for image retrieval using combined features is proposed. With the test for an image database including 766 general-purpose images and comparison and analysis of performance evaluation for features and similarity measures, our proposed retrieval approach demonstrates a promising performance. Experiment shows that combined features are superior to every single one of the three features in retrieval.     

Multi-object image retrieval based on shape and topology

We aim at developing a geometry-based retrieval system for multi-object images. We model both shape and topology of image objects including holes using a structured representation called curvature tree (CT); the hierarchy of the CT reflects the inclusion relationships between the objects and holes. To facilitate shape-based matching, triangle-area representation (TAR) of each object and hole is stored at the corresponding node in the CT. The similarity between two multi-object images is measured based on the maximum similarity subtree isomorphism (MSSI) between their CTs. For this purpose, we adapt a continuous optimization approach to solve the MSSI problem and a very effective dynamic programming algorithm to measure the similarity between the attributed nodes. Our matching scheme agrees with many recent findings in psychology about the human perception of multi-object images. Experiments on a database of 1500 logos and the MPEG-7 CE-1 database of 1400 shape images have shown the significance of the proposed method.     

基于兴趣点局部分布特征的图像检索方法

提出了一种基于兴趣点颜色和空间分布特征的图像检索方法。该方法把图像内容看作为由若干兴趣点组成的集合,首先利用小波系数的空间方向树特性来检测兴趣点,然后利用基于兴趣点的环形颜色直方图和空间离散度来描述图像的特征,最后用加权特征距离来估计图像内容的相似度。同时,通过利用环形颜色直方图和空间离散度作为图像特征保证了该算法能够对图像的尺度变化、旋转变化和平移变化具有很好的抑制能力。在含有1000幅图像的数据库上所做的一系列实验表明,该算法与其它基于兴趣点的方法相比,能够更准确和高效地查找出用户所需的图像,明显地提高了检索精度。     

一种改进的综合BTC颜色矩和DT-CWT的图像检索算法

摘 要：特征提取是基于内容的图像检索中的关键技术。针对基于单一特征检索效果不理想的问题,提出一种改进的综合颜色和纹理特征的图像检索算法。该算法在YIQ颜色空间中进行特征提取,首先结合方块编码（BTC）的思想,提取颜色矩作为颜色特征;采用双树复小波变换（DT-CWT）提取纹理特征,融合两种特征并利用相似性度量方式进行图像检索。实验结果表明算法所提取的颜色、纹理特征更利于检索,使用综合特征检索的平均查准率比同类算法更高。     

一种彩色图像文本自动定位算法

图像中的文本包含了重要的语义信息。提出了一种基于连通区域分析的彩色图像文本自动定位算法。首先对彩色图像进行多通道分解,得到少量代表颜色下的二值图像。对二值图像进行连通区域标记,并计算连通区域的属性参数。然后根据关于连通区域属性的先验知识限制,去掉连通区域中的非文本区域,其他的作为候选文本区域。最后根据文本行的排列规律从候选文本区域中提取文本行。实验结果表明该方法能较快地定位文本区域,定位精度较高。     

一种新的图像检索方法

常规基于内容图像检索的方法是提取图像的颜色、纹理等物理特征,运用相似性度量准则从图像库中查询相似的图像。为了提高图像检索的正确率,这里提出改进的方法。具体方法是:提取图像的物理特征,并将特征作为支持向量机(SVM)的输入向量,对图像进行分类,然后利用分类结果,对检索图像进行相似性匹配,从同类图像中找出相似的图像。实验结果显示,该方法的检索结果优于常规方法。     

Optimized hierarchical block matching for fast and accurate image registration

Recently the camera resolution has been highly increased, and the registration between high-resolution images is computationally expensive even by using hierarchical block matching. This paper presents a novel optimized hierarchical block matching algorithm in which the computational cost is minimized for the scale factor and the number of levels in the hierarchy. The algorithm is based on a generalized version of the Gaussian pyramid and its inter-layer transformation of coordinates. The search window size is properly determined to resolve possible error propagation in hierarchical block matching. In addition, we also propose a simple but effective method for aligning colors between two images based on color distribution adjustment as a preprocessing. Simplifying a general color imaging model, we show much of the color inconsistency can be compensated by our color alignment method. The experimental results show that the optimized hierarchical block matching and color alignment methods increase the block matching speed and accuracy, and thus improve image registration. Using our algorithm, it takes about 1.28 s for overall registration process with a pair of images in 5 mega-pixel resolution.