图像检索中的动态相似性度量方法

为提高图像检索的效率,近年来相关反馈机制被引入到了基于内容的图像检索领域。该文提出了一种新的相关反馈方法－－动态相似性度量方法。该方法建立在目前被广泛采用的图像相拟性度量方法的基础上,结合了相关反馈图像检索系统的时序特性,通过捕获用户的交互信息,动态地修正图像的相似性度量公式,从而把用户模型嵌入到了图像检索系统,在某种程度上使图像检索结果与人的主观感知更加接近。实验结果表明该方法的性能明显优于其它图像检索系统所采用的方法。     

结合边缘信息的图像检索技术

针对基于内容的图像检索技术中因忽略人眼视觉的影响而造成的检索率偏低问题,提出了一种新的图像检索算法.首先,结合人眼的视觉特性,借鉴方块编码的思想提取对表征图像内容有意义的子特征,该特征不仅体现了图像的边缘信息,而且在一定程度上对整幅图像的纹理特征也进行了较好地描述;在此基础上,定义图像的特征描述符.针对描述符自身的特点,采用图像的互信息作为最后的相似性度量准则.实验结果表明同以往算法相比,该算法具有较高的检索效率.     

一种新的颜色信息熵图像检索方法*

提出了一种新的相似性图像检索方法.首先分割图像,把一幅图像划分为若干子区域,抽取图像的分块颜色信息;再利用信息熵计算图像之间的颜色互信息.用这种方法对不同类型图像进行试验,结果表明,与其他颜色特征描述方法相比,该方法具有较好的效果.     

基于形状熵的图像检索方法*

提出了一种新的相似性图像检索方法.该方法利用信息熵理论,对图像的形状特征进行描述.首先分割图像,把一幅图像划分为若干子区域,抽取图像的主要形状并统计其所含的角度信息;再利用信息熵计算图像之间的形状互信息.用该方法对不同类型图像进行实验,结果表明,与其他方法相比其具有更好的效果,抗噪性强,也更稳定.     

基于分割和体素的医学图像配准方法

为了充分吸收基于分割和基于体素相似性两种配准方法的优点,提出了一种将分割和体素相结合的配准方法.该方法对待配准图像进行预处理,包括DICOM格式转换,图像去噪和图像分割,利用互信息作为衡量图像是否匹配的测度函数;最后采用优化策略来选择最佳图像变换.实验结果表明,该方法计算量小,速度快和精度高,有助于医疗诊断.     

一种新的文物图像检索方法

针对大学数字博物馆数据库中文物图像的特点,提出了一种新的文物图像检索方法。特征提取采用基于分块图像建立模糊颜色直方图;模糊颜色直方图的建立不仅考虑了不同颜色索引像素之间的差异,也考虑了同一颜色索引像素间的差异;图像分块策略结合了文物图像的颜色特征与形状特征。一种新的图像相似性度量方法---交互信息距离(MID)用来进行相似性匹配。参数AVRR/IAVRR用来进行检索性能评价,评价结果表明,本文的方法在文物图像检索中具有较高的检索准确率。     

交互信息理论及改进的颜色量化方法在图像检索中的应用研究

设计实现了基于内容的图像检索原型系统与检索性能评价系统,提出了一种基于交互信息与信息熵的图像相似性度量方法-交互信息距离（MID）和一种改进的颜色量化方法，成功地将两种方法应用于基于颜色特征的图像检索中，通过比较性研究证明，MID能较KLD提供更高的检索准确率；改进的颜色量化方法较基于HSV颜色空间的一致颜色量化方法有效地提高了检索准确率,试验证明，颜色空间的合理量化对图像检索有着重要影响，在选择颜色空间进行图像检索的同时，不能忽略对颜色空间的合理量化．     

基于归一化互信息与模糊自适应PSO的图像自动配准方法

提出了一种基于归一化互信息相似性判据,并采用模糊自适应粒子群优化算(particle swam optimization,PSO)作为搜索策略的图像自动配准方法.由于互信息方法不能解决图像缩放的问题,该方法在计算图像互信息之前,先对图像进行尺寸相同化操作;同时针对互信息方法中目标函数易陷入局部极值及搜索速度慢的问题,该方法采用归一化互信息作为相似性准则,并提出以模糊自适应PSO算法作为优化策略来提高配准速度和精度的方法.实验表明,采用归一化互信息作为配准测度,可提高配准的鲁棒性,而且,引入了模糊推理机之后,配准效率得到大幅提高,用该方法对具有仿射变换的图像进行配准能得到快速、精确的配准结果,证明了该算法的可行性和有效性.     

互信息启发的相似度组合图像检索算法

图像的视觉特征与用户描述之间的差距一直是影响基于内容的图像检索准确度的最主要因素。对多种相似度进行组合来检索图像是近几年图像检索领域涌现出的一个研究热点,也是缩小这种差距的一种有效途径。如何选择更好的组合方法则是该领域很多研究者关注的核心问题。提出一种新的相似度组合算法。该算法基于互信息度量相对熵的原理,计算连续变量相似度与离散变量相似性之间的相关性,对多种相似度进行选择,以“和规则”组合相似度。在公用数据集上进行检索实验,该算法优于当前其他的“和规则”下的组合方法。     

混合微结构分析方法及其在图像检索中的应用

为解决图像检索领域现有的特征提取方法信息保留不足,检索方法单一,以及时效性不强等问题,对一些独特的图像分析方法进行讨论.方法:利用一种新的图像特征侦测方法生成图像特征库,然后利用该方法提取检索图像内容特征,经过相似性度量准则度量后,最终以度量排序结果进行图像检索.结果:实验结果表明,与多种流行的图像检索方法相比,无论是在查准率还是在查全率方面该方法都具备较好的性能.结论:所述图像检索方法利用一种新的各向异性的微结构分析方法实现了对图像特征的描述,有效提高了图像检索能力,具有很强的实用性.     

A mutual information based face clustering algorithm for movie content analysis

This paper investigates facial image clustering, primarily for movie video content analysis with respect to actor appearance. Our aim is to use novel formulation of the mutual information as a facial image similarity criterion and, by using spectral graph analysis, to cluster a similarity matrix containing the mutual information of facial images. To this end, we use the HSV color space of a facial image (more precisely, only the hue and saturation channels) in order to calculate the mutual information similarity matrix of a set of facial images. We make full use of the similarity matrix symmetries, so as to lower the computational complexity of the new mutual information calculation. We assign each row of this matrix as feature vector describing a facial image for producing a global similarity criterion for face clustering. In order to test our proposed method, we conducted two sets of experiments that have produced clustering accuracy of more than 80%. We also compared our algorithm with other clustering approaches, such as the k-means and fuzzy c-means (FCM) algorithms. Finally, in order to provide a baseline comparison for our approach, we compared the proposed global similarity measure with another one recently reported in the literature.     

Ranking consistency for image matching and object retrieval

The goal of object retrieval is to rank a set of images by the similarity of their contents to those of a query image. However, it is difficult to measure image content similarity due to visual changes caused by varying viewpoint and environment. In this paper, we propose a simple, efficient method to more effectively measure content similarity from image measurements. Our method is based on the ranking information available from existing retrieval systems. We observe that images within the set which, when used as queries, yield similar ranking lists are likely to be relevant to each other and vice versa. In our method, ranking consistency is used as a verification method to efficiently refine an existing ranking list, in much the same fashion that spatial verification is employed. The efficiency of our method is achieved by a list-wise min-Hash scheme, which allows rapid calculation of an approximate similarity ranking. Experimental results demonstrate the effectiveness of the proposed framework and its applications.     

Medium-low resolution multisource remote sensing image registration based on SIFT and robust regional mutual information

Owing to significant geometric distortions and illumination differences, high precision and robust matching of multisource remote sensing images is a difficult task. To solve this, mutual information (MI)-based methods have been a preferred choice, as MI represents a measure of statistical dependence between the two images. However, MI only considers original grey information and neglects spatial information in the calculation of the probability distribution. In this paper, a novel similarity metric based on rotationally invariant regional mutual information (RIRMI) is proposed. The RIRMI metric is constructed by combining MI with a regional information based on the statistical relationship between rotationally invariant centre-symmetric local binary patterns of the images. The similarity metric based on RIRMI considers not only the spatial information, but the effect of the local grey variations and rotation changes on computing probability density function as well. The proposed method is tested on various simulated remote sensing images (5–30 m GSD) and real remote sensing images (2–30 m GSD) which are taken at different sensors, spectral bands, and times. Results verify that RIRMI is more robust and accurate than the common MI-based registration method.     

基于最大互信息的多模医学图象配准

介绍了一种基于最大互信息原理的图象配准技术,并就实施最大互信息配准法的一些重要技术问题进行了研究,其中包括不增加新数据点的格点采样子集、不产生分数灰度值的ＰＶ插值技术和出界点策略等。该方法在搜索策略上采用了无需计算梯度的Ｐｏｗｅｌｌ算法。由于计算互信息的关键技术与有效的搜索策略的结合,使得该方法能快速、准确地实现多模医学图象的配准。用该方法对７个病人的４１套ＣＴ－ＭＲ和３５套ＭＲ－ＰＥＴ３Ｄ全脑数     

Multimodality image registration by maximization of quantitative–qualitative measure of mutual information

This paper presents a novel image similarity measure, referred to as quantitative–qualitative measure of mutual information (Q-MI), for multimodality image registration. Conventional information measures, e.g., Shannon's entropy and mutual information (MI), reflect quantitative aspects of information because they only consider probabilities of events. In fact, each event has its own utility to the fulfillment of the underlying goal, which can be independent of its probability of occurrence. Thus, it is important to consider both quantitative (i.e., probability) and qualitative (i.e., utility) measures of information in order to fully capture the characteristics of events. Accordingly, in multimodality image registration, Q-MI should be used to integrate the information obtained from both the image intensity distributions and the utilities of voxels in the images. Different voxels can have different utilities, for example, in brain images, two voxels can have the same intensity value, but their utilities can be different, e.g., a white matter (WM) voxel near the cortex can have higher utility than a WM voxel inside a large uniform WM region. In Q-MI, the utility of each voxel in an image can be determined according to the regional saliency value calculated from the scale-space map of this image. Since the voxels with higher utility values (or saliency values) contribute more in measuring Q-MI of the two images, the Q-MI-based registration method is much more robust, compared to conventional MI-based registration methods. Also, the Q-MI-based registration method can provide a smoother registration function with a relatively larger capture range. In this paper, the proposed Q-MI has been validated and applied to the rigid registrations of clinical brain images, such as MR, CT and PET images.     

基于P样条和局部互信息的非刚性医学图像配准*

为了克服互信息仅考虑两幅图像相应像素的灰度信息,忽略了图像本身的内在空间信息,以及B样条变换模型存在形变场奇异点的缺陷,提出一种基于P样条和局部互信息的非刚性医学图像配准方法。该方法以局部互信息为相似性测度,采用P样条变换模型模拟待配准图像的几何形变,然后使用三次插值算法对图像像素进行赋值,结合对大规模参数优化效率高的LBFGS算法对配准参数进行优化。实验结果表明,该方法较传统的互信息和B样条变换模型都有效地提高了配准的精度。     

基于SIFT和最邻近匹配的商品图像相似度算法

在互联网电子商务领域中,随着电商用户量的激增,各种问题不断涌现。其中,同行业的卖家抄袭复制其他店铺信息的事件也经常发生,而抄袭的图像信息相较于文字信息更难以检测出相似性,因为抄袭者往往有可能会将图像信息进行剪裁、旋转、加滤镜,或者用PS等技术进行处理,使得处理后的图像不容易检测出与原图相似。而人工比对效率低下,且成本高,这就需要一个以能快速计算出商品图像相似度的算法为基础的系统来解决这个问题。SIFT(Scale-invariant Feature Transform)描述子具有尺度不变性,能够解决传统算法对于旋转后图像相似度较低的局限性,且该描述子所描述的特征信息量大。本文在介绍传统图像哈希算法的基础上,提出使用基于SIFT描述子的近似最邻近匹配算法用于电钻商品图像相似度比较。对电钻商品原图进行剪裁、增加滤镜、增加对比度、旋转和增加水印等操作生成新的图片,将这些新的图片分别和原图进行相似度对比。实验结果表明,基于SIFT描述子的近似最邻近匹配算法与哈希算法、原始SIFT算法相比有比较好的精度,能够比较准确地识别出抄袭的图像信息。     

Direct model based visual tracking and pose estimation using mutual information

This paper deals with model-based pose estimation (or camera localization). We propose a direct approach that takes into account the image as a whole. For this, we consider a similarity measure, the mutual information. Mutual information is a measure of the quantity of information shared by two signals (or two images in our case). Exploiting this measure allows our method to deal with different image modalities (real and synthetic). Furthermore, it handles occlusions and illumination changes. Results with synthetic (benchmark) and real image sequences, with static or mobile camera, demonstrate the robustness of the method and its ability to produce stable and precise pose estimations.     

Image database indexing using JPEG coefficients

Image database indexing is used for efficient retrieval of images in response to a query expressed as an example image. The query image is processed to extract information that is matched against the index to provide pointers to similar images. We present a technique that facilitates content similarity-based retrieval of jpeg-compressed images without first having to uncompress them. The technique is based on an index developed from a subset of jpeg coefficients and a similarity measure to determine the difference between the query image and the images in the database. This method offers substantial efficiency as images are processed in compressed format, information that was derived during the original compression of the images is reused, and extensive early pruning is possible. Initial experiments with the index have provided encouraging results. The system outputs a set of ranked images in the database with respect to the query using the similarity measure, and can be limited to output a specified number of matched images by changing the threshold match.