综合颜色和局部空间特征的彩色图像检索方法

针对图像颜色空间的分布特性,提出一种用颜色色调直方图来表达图像的颜色特征;通过HSV颜色空间的三个独立分量表示方法来描述颜色的空间特征,具有旋转、平移、尺度不变特性.实验表明,该方法很好地克服了颜色直方图和颜色矩无法表达图像色彩的空间位置的缺点,显示了组合特征的图像检索特征的有效性和较高的检索准确性.     

综合颜色和局部空间特征的彩色图像检索方法

针对图像颜色空间的分布特性,提出一种用颜色色调直方图来表达图像的颜色特征;通过HSV颜色空间的三个独立分量表示方法来描述颜色的空间特征,具有旋转、平移、尺度不变特性。实验表明,该方法很好地克服了颜色直方图和颜色矩无法表达图像色彩的空间位置的缺点,显示了组合特征的图像检索特征的有效性和较高的检索准确性。     

综合颜色和空间信息的图像检索

提出了一种组合颜色、空间信息的图像检索方法,用颜色块直方图来表达图像的颜色特征;通过提取颜色块的质心、分布方差聚散度等特征得到图像的空间特征。实验结果表明,基于组合特征的图像检索方法要优于单纯的基于颜色或者空间特征的方法。     

Parallel Local Histogram Comparison Hardware Architecture for Content-Based Image Retrieval

The explosive growth of the World Wide Web has made a vast amount of information available especially the multimedia data such as images and graphics. New generation search engines with the technology of Content-Based Image Retrieval (CBIR) is a response to the need. In this paper, a new technique based on composite local colour histograms suitable for CBIR is described. The proposed approach is accurate even in very large image databases. Furthermore, a novel parallel hardware structure has been designed and implemented on a chip-set of FPGAs, in order to increase the operation speed. Its typical maximum clock frequency is 35 MHz and it can perform over 50 comparisons of 640×480-pixel images per second.     

复杂度直方图及其在图像检索中的应用

该文提出了一种新的描述图像纹理信息的复杂度直方图特征,实验结果表明该特征用于图像检索可以取得较好的检索效果。文章同时将该特征与图像色彩直方图组合特征用于图像检索,实验结果证实使用组合特征可以得到更高的检索效率。     

Efficient Content-Based Image Retrieval through Metric Histograms

This paper presents a new and efficient method for content-based image retrieval employing the color distribution of images. This new method, called metric histogram, takes advantage of the correlation among adjacent bins of histograms, reducing the dimensionality of the feature vectors extracted from images, leading to faster and more flexible indexing and retrieval processes. The proposed technique works on each image independently from the others in the dataset, therefore there is no pre-defined number of color regions in the resulting histogram. Thus, it is not possible to use traditional comparison algorithms such as Euclidean or Manhattan distances. To allow the comparison of images through the new feature vectors given by metric histograms, a new metric distance function MHD( ) is also proposed. This paper shows the improvements in timing and retrieval discrimination obtained using metric histograms over traditional ones, even when using images with different spatial resolution or thumbnails. The experimental evaluation of the new method, for answering similarity queries over two representative image databases, shows that the metric histograms surpass the retrieval ability of traditional histograms because they are invariant on geometrical and brightness image transformations, and answer the queries up to 10 times faster than the traditional ones.     

一种基于多特征融合的彩色图像检索方法

提出了一种基于特征融合的图像检索方法。利用图像的HSV直方图特征建立图像颜色直方图,并采用直方图二次式距离公式取得图像相似性度量值;利用图像的纹理特征建立256维的LBP特征向量,并利用欧式距离取得相似性度量值;通过两特征融合的方法取得图像检索中关键图和检索图之间的相似度值,使得检索取得更好的效果。实验表明,在查准率和...     

基于内容的图像检索的相似度测量方法

图像特征匹配是基于内容的图像检索（Content based image retrieval,CBIR） 实现的一个关键环节,而图像特征的匹配主要依赖于图像特征之间的相似度测量。为了提高CBIR的检索性能,本文提出了一种有效的相似度测量方法——基于图像k近邻的相似度测量（Similarity measure based on k-nearest neighbors of images, SBkNN）方法。在该方法中,查询图像与被检索图像的相似度通过计算这两幅图像属于同一语义（无论是哪种语义）种类的联合概率来衡量,而此概率可分别通过分析这两幅图像与各自近邻图像的距离得到。最后利用Corel5k数据集对本文所提出的SBkNN方法和传统的相似度测量方法进行了对比。实验结果表明,在CBIR中使用本文提出的SBkNN方法,有效地提高了CBIR的检索性能。     

基于内容的图像检索技术

随着多媒体技术的迅速发展,图像数据库也急剧膨胀起来,如何高效、快速地从图像资源中获取有用的图像成了信息检索技术研究的热点。对近年来基于内容图像检索技术中的特征提取技术进行了详细全面的论述,并指出了下一步的研究方向。     

基于局部颜色空间特征的图像检索

基于图像中主要物体的尺寸动态地对图像分块来获得颜色的空间分布特征,并选取了符合人眼感知特性的HSV颜色空间进行非均匀量化,提取各个分块的主色及其所占的百分比作为颜色综合特征矢量,根据所提出的综合特征矢量给出相应的相似度计算方法,同时构造出相似矩阵进行基于颜色内容的图像检索,能通过调整各个分块的权值来进一步提高检索的精度,最后将文中提出的方法与传统的全局直方图方法相比较,可知文中的方法因为利用了动态分块的方法,考虑了颜色的空间信息以及权值的调整,从而取得了比较好的检索结果。     

基于MPEG-7多特征融合的图像检索技术

针对MPEG-7单一描述符特征检索效率低的问题,提出了主颜色和边缘直方图特征融合的检索算法。同时利用检索目标在图像中分布的特点,利用分块加权法来提高对检索主目标的检索准确度。实验表明,该方法在查准率和查全率均优于单一特征的检索算法,检索精度得到了提高。     

基于对象空间关系的图像检索方法研究

描述了图像中对象之间的拓扑关系、投影间隔关系和角度关系,给出了不同空间关系距离的定义,提出了集成查询图像与数据库图像中共有对象数目和空间关系的图像相似性度量方法。实验证明,利用该相似性度量方法实现的图像检索系统的检索精度是较高的。     

Approximating Content-Based Object-Level Image Retrieval

Object-level image retrieval is an active area of research. Given an image, a human observer does not see random dots of colors. Rather, he/she observes familiar objects in the image. Therefore, to make image retrieval more user-friendly and more effective and efficient, object-level image retrieval technique is necessary. Unfortunately, images today are mostly represented as 2D arrays of pixels values. The object-level semantics of the images are not captured. Researchers try to overcome this problem by attempting to deduce the object-level semantics through additional information such as the motion vectors in the case of video clips. Some success stories have been reported. However, deducing object-level semantics from still images is still a difficult problem. In this paper, we propose a color-spatial approach to approximate object-level image retrieval. The color and spatial information of the principle components of an object are estimated. The technique involves three steps: the selection of the principle component colors, the analysis of spatial information of the selected colors, and the retrieval process based on the color-spatial information. Two color histograms are used to aid in the process of color selection. After deriving the set of representative colors, spatial knowledge of the selected colors is obtained using a maximum entropy discretization with event covering method. A retrieval process is formulated to make use of the spatial knowledge for retrieving relevant images. A prototype image retrieval tool has been implemented on the Unix system. It is tested on two image database consisting of 260 images and 11,111 images respectively. The results show that the color-spatial approach is able to retrieve similar objects with much better precision than the sole color-based retrieval methods.     

基于内容的图像检索介绍

基于内容特征的图象检索(CBIR)是目前国内外研究的一个热点。本文简要介绍了基于内容的图像检索技术的发展过程及主要原理,重点论述了基于内容的图像检索常用关键技术——图像视觉特征的描述和提取。     

基于内容的图像检索介绍

基于内容特征的图象检索（CBIR）是目前国内外研究的一个热点。本文简要介绍了基于内容的图像检索技术的发展过程及主要原理，重点论述了基于内容的图像检索常用关键技术——图像视觉特征的描述和提取。     

一种新的基于区域的图像检索方法

论文提出了一种新的基于区域的图像检索方法。与传统的基于区域的检索方法相比,论文从组成目标对象的基本结构角度出发分割图像,利用少量色彩等级更易描述对象主要构成的特性提取一组能够描述对象基本组成的区域序列。采用这些区域列的面积作为图像特征,用于图像检索。实验表明,该算法简单但非常有效,而且对图像的旋转、尺度变化具有良好的鲁棒性。     

MUSE: A Content-Based Image Search and Retrieval System Using Relevance Feedback

The field of Content-Based Visual Information Retrieval (CBVIR) has experienced tremendous growth in the recent years and many research groups are currently working on solutions to the problem of finding a desired image or video clip in a huge archive without resorting to metadata. This paper describes the ongoing development of a CBVIR system for image search and retrieval with relevance feedback capabilities. It supports browsing, query-by-example, and two different relevance feedback modes that allow users to refine their queries by indicating which images are good or bad at each iteration.     

基于内容的图像检索方法的探讨

该文在对图像特征分析及相似检索方法研究分析的基础上,提出了一种基于色调局部累加直方图与纹理特征结合的相似性匹配度量算法。该算法不但缩短了查询时间,而且通过实验证明,该算法有较高的检索率。     

改进的基于直方图的图像检索方法

针对传统色彩直方图检索方法容易丢失空间信息的问题。提出了一种改进方法．该方法进一步分析了具有相同灰度的像素之间在空间分布上的差异，用其在图像中形成的主要连通区域的面积大小代替传统直方图中使用的数量总和指标．从而能够间接反映色彩的空间分布特征。提高检索质量，对纹理丰富的图像的检索效果尤其显著．     

基于内容的图像检索技术

随着多媒体技术的迅速发展,传统的基于关键字的信息检索技术已逐渐不能满足要求,因此,基于内容的图像检索技术成为当今的一个研究热点.介绍了基于内容图像检索系统的基本组成.综述了基于颜色、纹理、形状、语义等图像检索技术的主要方法.分析和比较了现有的各种图像检索技术的方法.讨论了相关反馈技术、检索性能的评价等CBIR研究中的关键问题.同时指出了CBIR研究中存在的问题,以及未来的发展趋势和研究方向.