综合颜色及空间特征的图象检索方法

针对目前的CBIR,没能有效地结合图象中对象空间信息的问题,提出了一种新的图象分割方法,该方法以线性加权方式融合颜色、纹理特征采用区域增长法对图象进行分割,避免了传统的由于单独采用颜色或纹理特征分割图象而产生的欠分割或过分割问题.根据分割区域的灰度、形状、位置等特征计算图象间的相似度,提出采用图象的灰度特征代替彩色信息以提高查询速度.     

4种矩描述子在形状检索中的性能比较

在基于内容的图象检索(CBIR)中,形状是图象的主要低层特征之一.人们虽已经提出了许多表示形状的方法,然而大多数方法都要假定知道形状的边界信息,在一般情况下,形状的边界是无法获取的.基于区域的描述子不但适用于一般形状,而且具有很强的抗噪声和抗失真能力.通过对下列4种区域形状描述符的研究和比较几何不变矩(GMD),Legendre矩(LMD)、Zernike矩(ZMD)以及正交的Fourier-Mellin矩(OFMMD),并在一个由400多幅图象组成的形状库上进行的检索试验表明,从计算复杂度、稳健性、分层表示以及检索性能来考虑,Zernike矩是4种矩当中最适合于进行形状检索的描述符.     

通过区域约束和相关性反馈提高检索性能

在已有的基于内容的图像检索(CBIR)系统中，颜色、纹理、形状等全局特征被广泛使用。这些全局特征从整幅图象中提取。实现简单、计算速度快。但由于它们缺乏对图象中物体的属性和物体间关系的描述能力，从而影响了检索的性能。相反地，从图象的局部区域提取区域、明显点等局部特征，它们和图象中的物体有着紧密的联系，因而能够更好地描述图象的内容，可以缩小低水平视觉特征与用户高水平语义查询概念间的巨大差异。     

一种基于颜色特征的图象检索方法

提出了一种改进的基于颜色感知特征和图象检索方法。这种方法采用符合人类视觉特征的ＨＳＶ颜色空间来表示图象的颜色特性,利用人对颜色的感知来对颜色分量进行等间隔的量化工形成特征矢量,用直方图交叉算法进行图象的匹配。用户可以通过示例方式,指定颜色或指定区域来表示查询的视觉要求。     

环球网图象搜索引擎研究综述

提出了建立一个ＷＷＷ图象搜索引擎的方案，搜索引擎在Ｉｎｔｅｒｎｅｔ上浏览主页，对遇到的图象进行特征计算，抽取出索引信息，存储索引向量，根据图象内容进行相似图象的查找。分析了颜色直方图、边缘方向直方图、纹理分析和形状不变性等基于图象内容查询的技术，并对ＷＷＷ图象搜索引擎的发展作出了预测。     

一种面向图象语义的主要区域提取方法

图象主要区域的提取是图象语义抽取及其应用的基础 .为了更好地进行图象语义的抽取 ,提出了一种面向图象语义的图象主要区域自动提取方法 .该方法首先将图象划分成固定大小的子块 ,并通过对子块特征进行聚类来获得图象的初始区域分割 ;而后 ,经过一系列的后处理来优化分割结果 ,并实现前景和背景区分 ;最后通过分析每个背景区域的重要程度 ,去除掉不相关的背景区域 .通过对包含有显著对象的户外图象进行的实验表明 :该方法不仅可以去除图象中 ,大量与图象语义不相关的内容 ,而且能保留图象的主要信息 ,这就为进一步的图象语义应用打好了基础 .     

一种图象区域颜色与面积特征的抽取与表示方法

图象色彩与面积特征已被广泛运用于基于内容的图象或视频检索等领域.如何获取及表示区域彩色特征相似性,是对彩色图象进行有效分割的前提.此文提出一种基于HSI颜色空间的区域彩色特征相似性表示方法,利用改进的区域合并法对彩色图象进行分割.在图象分割的基础上,进而研究并提出一种图象分割区域颜色与面积特征的抽取与表示的有效方法.     

距离分布直方图及其在商标图案检索中的应用

形状是描述图象的重要视觉特征,它可以通过像素点分布在空间不同的区域而表现出来,针对二值图象提出了一种基于区域的形状特征,即距离分布直方图,它的基本思想就是通过统计图象中像素点的距离区域的分布情况来获得形状特征。其中,基准点的选择和距离区域的划分是两具重要的部分,实验结果表明,距离分布直方图能够有效地刻画出二值图象的形状特征,并且具有非常好的平移。尺度和旋转不变性,将其应用于商标图案检索,其检索结果符合人眼的视觉感受。     

图象数据库查询语言PSEQ

图象查询语言是图象数据库设计的重要方面,因需涉及除关系代数以外的图象代数运算,故常设计成关系型查询语言的扩充。本文介绍的PSEQ通过扩充传统的关系数据库模式,使框架等抽象数据类型(ADTs)可作为关系中的域类型,以利于表达图象实体及其空间关系和语义等信息。PSEQ能实现对图象信息在三个不同层次上的查询:对关     

遥感地形图象的灰度与分形分析

为了能够更加充分、准确地在遥感地形图象中利用分形信息,需要将其与其他特征配合使用。文章通过从视觉成像的角度,深入地剖析物体不同区域的形状和位置对成像灰度的影响,进而将有关结论与图象分形指数数据相结合,应用于基于图象内容的遥感图象的区域(标识)分割,取得了令人满意的效果,从中检查出分形数据中对应于图象深灰度区无意义的数值部分,从而保证了分形数据的可靠性,同时也说明了结合灰度或其它信息于分形图象应用的图象分析方式是十分必要的。     

Adaptive image segmentation for region-based object retrieval using generalized Hough transform

Finding an object inside a target image by querying multimedia data is desirable, but remains a challenge. The effectiveness of region-based representation for content-based image retrieval is extensively studied in the literature. One common weakness of region-based approaches is that perform detection using low level visual features within the region and the homogeneous image regions have little correspondence to the semantic objects. Thus, the retrieval results are often far from satisfactory. In addition, the performance is significantly affected by consistency in the segmented regions of the target object from the query and database images. Instead of solving these problems independently, this paper proposes region-based object retrieval using the generalized Hough transform (GHT) and adaptive image segmentation. The proposed approach has two phases. First, a learning phase identifies and stores stable parameters for segmenting each database image. In the retrieval phase, the adaptive image segmentation process is also performed to segment a query image into regions for retrieving visual objects inside database images through the GHT with a modified voting scheme to locate the target visual object under a certain affine transformation. The learned parameters make the segmentation results of query and database images more stable and consistent. Computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy, robustness, and execution speed.     

Graphical Search for Images by <Emphasis Type="Italic">PictureFinder</Emphasis>

The text searching paradigm still prevails even when users are looking for image data for example in the Internet. Searching for images mostly means searching on basis of annotations that have been made manually. When annotations are left empty, which is usually the case, searches on image file names are performed. This may lead to surprising retrieval results. The graphical search paradigm, searching image data by querying graphically, either with an image or with a sketch, currently seems not to be the preferred method partly because of the complexity in designing the query.In this paper we present our PictureFinder system, which currently supports “full image retrieval” in analogy to full text retrieval. PictureFinder allows graphical queries for the image the user has in his mind by sketching colored and/or textured regions or by whole images (query by example). By adjusting the search tolerances for each region and image feature (i.e. hue, saturation, lightness, texture pattern and coverage) the user can tune his query either to find images matching his sketch or images which differing from the specified colors and/or textures to a certain degree. To compare colors we propose a color distance measure that takes into account the fact that different colors spread differently in the color space, and which take into account that the position of a region in an image may be important.Furthermore, we show our query by example approach. Based on the example image chosen by the user, a graphical query is generated automatically and presented to the user. One major advantage of this approach is the possibility to change and adjust a query by example in the same way as a query which was sketched by the user. By deleting unimportant regions and by adjusting the tolerances of the remaining regions the user may focus on image details which are important to him.     

WALRUS: a similarity retrieval algorithm for image databases

Approaches for content-based image querying typically extract a single signature from each image based on color, texture, or shape features. The images returned as the query result are then the ones whose signatures are closest to the signature of the query image. While efficient for simple images, such methods do not work well for complex scenes since they fail to retrieve images that match the query only partially, that is, only certain regions of the image match. This inefficiency leads to the discarding of images that may be semantically very similar to the query image since they may contain the same objects. The problem becomes even more apparent when we consider scaled or translated versions of the similar objects. We propose WALRUS (wavelet-based retrieval of user-specified scenes), a novel similarity retrieval algorithm that is robust to scaling and translation of objects within an image. WALRUS employs a novel similarity model in which each image is first decomposed into its regions and the similarity measure between a pair of images is then defined to be the fraction of the area of the two images covered by matching regions from the images. In order to extract regions for an image, WALRUS considers sliding windows of varying sizes and then clusters them based on the proximity of their signatures. An efficient dynamic programming algorithm is used to compute wavelet-based signatures for the sliding windows. Experimental results on real-life data sets corroborate the effectiveness of WALRUS'S similarity model.     

基于物理模型的彩色图像分割

本文给出一个基于光学物理模型的真实彩色图像分割算法.算法首先对图像上的颜色变 化(由光照和物体颜色引起)进行分析与综合,然后分割图像.算法的基础是双色反射模型理 论,该理论认为反射光的颜色是界面反射(耀斑颜色)和本体反射(物体颜色)的线性组合,这两 种反射光在颜色空间的三维直方图中形成特定的聚类(点簇).因此分析聚类的性质可帮助确 定光照和物体的颜色,但是有意义的聚类的生成又以图像中物体区域的确定为前提.算法按 照假设检验的策略,依据图像中的连通性和颜色空间中聚类的特征,完成彩色图像的分割,并 产生对景物中所发生的光学过程的物理描述.该描述包括本征反射图像、分割图像、物体和光 照颜色的符号描述.本征反射图像包括只反映界面反射的耀斑图像和从原图像中去除耀斑影 响后的本体图像.     

Pictorial Query Specification for Browsing Through Spatially Referenced Image Databases

A pictorial query specification technique that enables the formulation of complex pictorial queries for browsing through a collection of spatially referenced images is presented. It is distinguished from most other methods by the fact that in these methods the query image specifies a target database image in its entirety whereas in our approach the query image specifies the combination of objects that the target database image should contain rather than being treated as a whole image. The query objects are represented by shape features although other features such as color, texture or wavelets could also be used. Using our technique, it is possible to specify which particular objects should appear in the target images as well as how many occurrences of each object are required. Moreover, it is possible to specify the minimum required certainty of matching between query-image objects and database-image objects, as well as to impose spatial constraints that specify bounds on the distance between objects and the relative direction between them. These spatial constraints can also be used to specify other topological relations such as enclosure, intersection, overlap, etc. Each pictorial query is composed of one or more query images. Each query image is constructed by selecting the required query objects and positioning them according to the desired spatial configuration. Boolean combinations of two or more query images are also possible by use of AND and OR operators. A query image may be negated in order to specify conditions that should not be satisfied by the database images that are retrieved successfully. In addition, a capability is provided to specify whether the same instance of an object is to be used when it appears in more than one of the query images that make up the pictorial query, or whether two different instances are allowed. Several example queries are given that demonstrate the expressive power of this query specification method. An algorithm for retrieving all database images that conform to a given pictorial query specification is presented. The user interface for using this pictorial query specification method to browse the results in a map image database application is described and illustrated via screen shots.     

基于目标区域的语义图像检索

提出了一种基于高层语义的图像检索方法,该方法首先将图像分割成区域,提取每个区域的颜色、形状、位置特征,然后使用这些特征对图像对象进行聚类,得到每幅图像的语义特征向量;采用模糊C均值算法对图像进行聚类,在图像检索时,查询图像和聚类中心比较,然后在距离最小的类中进行检索。实验表明,提出的方法可以明显提高检索效率,缩小低层特征和高层语义之间的"语义鸿沟"。     

面向2D对象形状识别的空间模糊查询研究

基于空间形状的查询与认知主体的空间推理密切相关。从空间认知的角度,通常希望查询结果是一类形状结构相似的对象集合。以形状的不确定性表达和模糊查询为研究内容,提出一种面向2D对象形状识别的空间查询方法——向心包络算法。算法将对象划分为以最大内径中心为公共点的三角形集合,在此基础上建立相应的形状度量方法,通过提取所有顶点关于对象整体结构的形状影响因子求得对象之间的形状相似度,并建立与模糊形状谓词的匹配关系。实验表明,该方法可以实现2D对象的空间模糊查询,且查询结果与模糊形状谓词基本一致。     

Spatial Color Indexing and Applications

We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors and when computed efficiently, turns out to be both effective and inexpensive for content-based image retrieval. The correlogram is robust in tolerating large changes in appearance and shape caused by changes in viewing position, camera zoom, etc. Experimental evidence shows that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval. We also provide a technique to cut down the storage requirement of the correlogram so that it is the same as that of histograms, with only negligible performance penalty compared to the original correlogram.We also suggest the use of color correlogram as a generic indexing tool to tackle various problems arising from image retrieval and video browsing. We adapt the correlogram to handle the problems of image subregion querying, object localization, object tracking, and cut detection. Experimental results again suggest that the color correlogram is more effective than the histogram for these applications, with insignificant additional storage or processing cost.