Query-Sensitive Similarity Measures for Information Retrieval

The application of document clustering to information retrieval has been motivated by the potential effectiveness gains postulated by the cluster hypothesis. The hypothesis states that relevant documents tend to be highly similar to each other and therefore tend to appear in the same clusters. In this paper we propose an axiomatic view of the hypothesis by suggesting that documents relevant to the same query (co-relevant documents) display an inherent similarity to each other that is dictated by the query itself. Because of this inherent similarity, the cluster hypothesis should be valid for any document collection. Our research describes an attempt to devise means by which this similarity can be detected. We propose the use of query-sensitive similarity measures that bias interdocument relationships toward pairs of documents that jointly possess attributes expressed in a query. We experimentally tested three query-sensitive measures against conventional ones that do not take the query into account, and we also examined the comparative effectiveness of the three query-sensitive measures. We calculated interdocument relationships for varying numbers of top-ranked documents for six document collections. Our results show a consistent and significant increase in the number of relevant documents that become nearest neighbors of any given relevant document when query-sensitive measures are used. These results suggest that the effectiveness of a cluster-based information retrieval system has the potential to increase through the use of query-sensitive similarity measures.     

一种快速旋转不变图像检索新方法

提出了一种新型快速旋转不变图像检索新方法.该方法首先对图像进行傅里叶变换和功率谱分解,提取功率谱的扇形区域能量和环形区域能量参数,并将其均值和标准差作为图像纹理特征.然后,利用谱能量分布特征把纹理的主方向旋转到0°,提取旋转后图像的共生矩阵参数和小波分解各子带图像统计参数作为基本特征.利用所提出的特征提取方法在两组分别包含25类单色自然纹理的图像库上进行检索试验.结果表明,该方法获得了良好的检索效果.     

Learning Robust Similarity Measures for 3D Partial Shape Retrieval

In this paper, we propose a novel approach to learning robust ground distance functions of the Earth Mover’s distance to make it appropriate for quantifying the partial similarity between two feature-sets. First, we define the ground distance as a monotonic transformation of commonly used feature-to-feature base distance (or similarity) measures, so that in computing the Earth Mover’s distance, the algorithm could better turn its focus on the feature pairs that are correctly matched, while being less affected by irrelevant ones. As a result, the proposed method is especially suited for 3D partial shape retrieval where occlusion and clutter are serious problems. We prove that when the transformation satisfies certain conditions, the metric property of the base distance is sufficient to guarantee the ground distance is a metric (and so is the Earth Mover’s distance), which makes fast shape retrieval on large databases technically possible. Second, we propose a discriminative learning framework to optimize the transformation function based on the real Adaboost algorithm. The optimization is performed in the space of the piecewise constant approximations of the transformation without making any parametric assumption. Finally, extensive experiments on 3D partial shape retrieval convincingly demonstrate the effectiveness of the proposed techniques.     

一种新颖的医学图像建模及相似性搜索方法

医学图像采集和存储技术的发展导致了相关数据的飞速增长,这些医学图像数据能够有效地辅助医生进行精确的诊断,但由于医学图像在相似性搜索方面要求图像匹配的精度远高于普通图像,所以目前仍没有一种有效的方法解决医学图像的相似性搜索问题.文中首先提出一种不确定定点图模型(ULG,Uncertain Location graph),并针对脑部CT图像的固有特点,提出一种基于脑部CT图像纹理的从图像到不确定定点图的建模方法,继而提出一种基于不确定定点图的相似性搜索算法,并通过一种有效的索引结构,有效地减少了无意义的查询处理,降低了搜索时间.实验结果表明,该方法可以更精确地找出具有相似纹理的脑部CT图像.     

基于词典优化与空间一致性度量的目标检索

基于视觉词典模型(bag of visual words model, BoVWM)的目标检索存在时间效率低、词典区分性不强的问题,以及由于空间信息的缺失及量化误差等导致的视觉语义分辨力不强的问题.针对这些问题,提出了基于词典优化与空间一致性度量的目标检索方法.首先,该方法引入E\\+2LSH(exact Euclidean locality sensitive hashing)过滤图像中的噪声和相似关键点,提高词典生成效率和质量;然后,引入卡方模型(chi-square model, CSM)移除词典中的视觉停用词增强视觉词典的区分性;最后,采用空间一致性度量准则进行目标检索并对初始结果进行K-近邻(K-nearest neighbors, K-NN)重排序.实验结果表明：新方法在一定程度上改善了视觉词典的质量,增强了视觉语义分辨能力,进而有效地提高目标检索性能.     

Differential and Numerically Invariant Signature Curves Applied to Object Recognition

We introduce a new paradigm, the differential invariant signature curve or manifold, for the invariant recognition of visual objects. A general theorem of É. Cartan implies that two curves are related by a group transformation if and only if their signature curves are identical. The important examples of the Euclidean and equi-affine groups are discussed in detail. Secondly, we show how a new approach to the numerical approximation of differential invariants, based on suitable combination of joint invariants of the underlying group action, allows one to numerically compute differential invariant signatures in a fully group-invariant manner. Applications to a variety of fundamental issues in vision, including detection of symmetries, visual tracking, and reconstruction of occlusions, are discussed.     

一种基于对象空间关系的图像检索模型

介绍一种基于空间相似的图像数据库检索模型，提出了空间相似度计算的算法，对对象的空间方位、拓扑关系、数量和空间关系局部高度相似等因素都作了全面考虑，详细阐述空间相似度与局部相似对象簇的求解过程，建立的模型不仅能识别经平移、缩放和旋转交换的图像，而且能识别图像的对称变换，通过实验验证，表明检索模型的图像搜索效率高。     

Extensions of Invariant Signatures for Object Recognition

A refinement of the method of differential invariant signatures for object recognition is presented. The value of the method lies in its compromise between local and global identifying properties, thereby allowing us to distinguish non-congruent curves whose Euclidean signatures have identical trace.     

相似度保持跨模态哈希检索

由于不同模态数据之间的异构性以及语义鸿沟等特点,给跨模态数据分析带来巨大的挑战.本文提出了一个新颖的相似度保持跨模态哈希检索算法.利用模态内数据相似性结构使得模态内相似的数据具有相似的残差,从而保证学习到的哈希码能够保持模态内数据的局部结构.同时利用模态间数据的标签,使得来自于不同模态同时具有相同标签的数据对应的哈希码...     

基于多尺度通用傅里叶描述子的灰度图像检索

灰度图像相似性匹配是图像检索系统的一个重要组成部分。为了把多尺度分析和通用傅里叶描述子相结合来得到多尺度通用傅里叶描述子，并将其用于灰度图像检索系统对。实验结果表明，多尺度通用傅里叶描述子具有多尺度分析效果，不仅能较好地克服图像灰度噪声带来的干扰，并且具有旋转、比例不变性，在灰度图像检索中能取得较好的结果。     

小波变换在仿射不变性目标识别中的应用

寻找相对于平移、尺度、旋转、扭曲不变的仿射不变量是现今多尺度分析在模式识别中应用的关键性问题.本文以文献[1]定义的仿射不变量为基础,对原文给出的绝对仿射不变量进行一定改进.通过分析,指出原文中所给绝对仿射不变量存在的缺陷,定义一种新的绝对仿射不变量.实验结果和分析表明,本文定义的绝对仿射不变量可以较好地用于目标物体识别.     

Mining Object Similarity for Predicting Next Locations

Next location prediction is of great importance for many location-based applications. With the virtue of solid theoretical foundations, Markov-based approaches have gained success along this direction. In this paper, we seek to enhance the prediction performance by understanding the similarity between objects. In particular, we propose a novel method, called weighted Markov model (weighted-MM), which exploits both the sequence of just-passed locations and the object similarity in mining the mobility patterns. To this end, we first train a Markov model for each object with its own trajectory records, and then quantify the similarities between different objects from two aspects: spatial locality similarity and trajectory similarity. Finally, we incorporate the object similarity into the Markov model by considering the similarity as the weight of the probability of reaching each possible next location, and return the top-rankings as results. We have conducted extensive experiments on a real dataset, and the results demonstrate significant improvements in prediction accuracy over existing solutions.     

噪声不敏感的柱状图特征描述符及其在图像检索中的应用

基于内容的图像检索方法往往将图片的内容表示成柱状图,根据图片柱状图之间的相似性进行图片的检索。数码图片中包含的噪声使得柱状图变得平滑,从而使图片之间变得更为相似,这增加了返回结果中包含的图片数量。为了进一步提高图片检索的性能,提出了一种对噪声不敏感的柱状图特征描述符,并应用该特征描述符进行图像之间的相似性匹配。首先将图片中的噪声描述为平稳附加高斯白噪声,并给出了相应的柱状图表示;然后通过随机变量的原点矩定义了柱状图的特征描述符,并分析了如何应用特征描述符恢复原始图片的柱状图。在算法的性能验证过程中,将提出的方法与4种相关算法进行比较,应用两个真实的图片数据库的图像检索实验验证了所提方法的有效性。     

Improvement on Higher-Order Neural Networks for Invariant Object Recognition

The higher order neural network(HONN) was proved to be able to realize invariant object recognition. By taking the relationship between input units into account, HONN's are superior to other neural models in invariant pattern recognition. However, there are two main problems preventing HONN's from practical applications. One is the combinatorial increase of weight number, that is, as input size increases, the number of weights in a HONN increases exponentially. The other problem is sensitivity to distortion and noise. In this paper, we described a method, in which by modifying the constraints imposed on the weights in HONN's, the performance of a HONN with respect to distortion can be improved considerably.     

不变矩稳定性及基于多级特征模型的目标识别研究

从成像过程和实际应用角度研究影响目标不变矩稳定性的各种因素,包括有限观测区域、高斯模糊和离散化处理以及在这些因素影响下不变矩的变化规律和误差．理论分析和实验还证明各不变矩自身的稳定特性不仅与阶数而且与其次数有关．这些研究和分析表明建立多尺度目标特征模型的必要性,可为不变矩在实际成像识别系统中正确有效地应用提供重要的理论和实验依据．     

基于仿射不变矩的神经网络目标识别

主要讨论了仿射不变矩及人工神经网络在模式识别中的应用,即使用BP网及BPX算法来模仿分类器,同时提取图像的3阶仿射不变矩作为特征输入建立对飞机图像的识别系统。     

一种实体运动模式相似性度量方法的研究

运动模式相似性度量在实际工作有广泛应用.提出了一种利用序列分析手段来实现实体运动模式相似性度量的算法.序列分析方法用于提取序列各种特征,然后利用上述特征分别对运动序列中的位置序列、速度序列、方向序列进行相似性度量,再将度量综合起来作为最终的运动模式相似性度量方法.最后,仿真研究表明,算法可以比较有效度量各种运动模式之间的相似性,从而町以应用于各项需要进行运动模式相似性度量的工作.