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.     

Development of a sensor for surface state measurements using experimental and numerical modal analysis

The characterization of the surface state becomes an increasing necessity for many industries. This paper presents a patented measurement method [M.A. Bueno, S. Fontaine, M. Renner, Dispositif Pour Évaluer L’état de Surface d’un Matériau et Procédé de Mise en œuvre Dudit Dispositif, Patent No. PCT/FR01/01770, 2000], which is able to evaluate the surface state of materials. This method gives roughness–friction criteria and is based on the principle of a tribometer of type “blade-disc”, where the analysed surface is the disc. The sensor is an active element, which vibrates according to its vibrating modes. This work shows how experimental and numerical analysis has allowed optimizing its gage instrumentation and its geometry. Moreover, the modal analysis and the building of a numerical model have set up the coupling between mechanical solicitations of the tested surface and mechanical responses of the sensor in each mode. Finally, an example of measurement of known surfaces is illustrated to explain the results of this study and the efficiency of this measurement method.     

基于内容的图像检索技术研究

介绍了基于内容的图像检索与文本关键词检索的不同，阐述了基于内容图像检索的基本原理、检索方式和关键技术以及医学图像检索方法。     

小波分析在基于内容的图像检索技术中的应用

简要介绍了小波分析的基本理论及基于内容的图像检索技术,重点阐述了小波分析在图像的特征提取(纹理、目标形状等)上的应用,并展望了基于内容的图像检索技术今后的主要研究方向。     

Adaptive segmentation of noisy and textured images

An image segmentation algorithm is described which is based on the integration of signal model parameter estimates and maximum a posteriori labelling. The parameter estimation is based on either a maximum likelihood-based method for a quadric signal model or a maximum pseudo-likelihood based method for a Gauss-Markov signal model. The first case is applicable to standard grey-level image segmentation as well as segmentation of shaded 3D surfaces, while the second case is applicable to texture segmentation. A key aspect of the algorithm is the incorporation of a coarse to fine processing strategy which limits the search for the optimum labelling at any one resolution to a subset of labellings which are consistent with the optimum labelling at the previous coarser resolution. Consistency is in terms of a prior label model which specifies the conditional probability of a given label in terms of the labelling at the previous level of resolution. It is shown how such an approach leads to a simple relaxation procedure based on local pyramid node computations. An extension of the algorithm is also described which performs accurate inter-region boundary placement using a step-wise refinement procedure based on a simple adaptive filter. The problem of automatic determination of the number of regions is also addressed. It is shown how a simple agglomerative clustering idea, again based on pyramid node computations, can effectively solve this problem.     

基于小波分解的纹理图像检索

纹理相似性研究是基于内容检索研究中的一个重要组成部分。采用树状小波分解的方法在相应的能量准则下对图像进行分解，并使用了主成分分析算法来压缩特征向量的维数，而且结合Tamura等人提出的、与人类视觉感知相对应的3个重要纹理特征来形成对图像进行精确描述的特征矢量。实验结果表明，该算法具有较强的应用价值。     

Automatic Annotation and Retrieval of Images

Although a variety of techniques have been developed for content-based image retrieval (CBIR), automatic image retrieval by semantics still remains a challenging problem. We propose a novel approach for semantics-based image annotation and retrieval. Our approach is based on the monotonic tree model. The branches of the monotonic tree of an image, termed as structural elements, are classified and clustered based on their low level features such as color, spatial location, coarseness, and shape. Each cluster corresponds to some semantic feature. The category keywords indicating the semantic features are automatically annotated to the images. Based on the semantic features extracted from images, high-level (semantics-based) querying and browsing of images can be achieved. We apply our scheme to analyze scenery features. Experiments show that semantic features, such as sky, building, trees, water wave, placid water, and ground, can be effectively retrieved and located in images.     

基于聚类的图像视觉内容检索和索引

图像是一种典型的可以大量获取的多媒体数据,对它们进行内容管理具有实际意义,描述在BOIC系统中提出并实现的基于聚类机制的图像视觉内容检索和索引方法．首先给出以视觉特征、空间结构、语义注释等来表示图像内容的模型;然后给出基于该模型的三个检索算法,包括基于视觉感知的颜色检索算法、轮廓检索算法、主色调扩展检索算法;最后给出采用集簇算法时媒体数据进行聚类的索引机制．它建立聚类索引表来缩小查询范围,从而提高了检索效率。     

Modeling and Retrieval of Moving Objects

This paper presents a symbolic formalism for modeling and retrieving video data via the moving objects contained in the video images. The model integrates the representations of individual moving objects in a scene with the time-varying relationships between them by incorporating both the notions of object tracks and temporal sequences of PIRs (projection interval relationships). The model is supported by a set of operations which form the basis of a moving object algebra. This algebra allows one to retrieve scenes and information from scenes by specifying both spatial and temporal properties of the objects involved. It also provides operations to create new scenes from existing ones. A prototype implementation is described which allows queries to be specified either via an animation sketch or using the moving object algebra.     

Recognition of coloured and textured images through a multi-scale neural architecture with orientational filtering and chromatic diffusion

The aim of this paper is to outline a multiple scale neural model to recognise colour images of textured scenes. This model combines colour and textural information in order to recognise colour texture images through the operation of two main components: a segmentation component composed of the colour opponent system (COS) and the chromatic segmentation system (CSS); and a recognition component formed by an ARTMAP-based neural network with scale and orientation-invariance properties. Segmentation is achieved by perceptual contour extraction and diffusion processes on the colour opponent channels based on the human psychophysical theory of colour perception. This colour regions enhancement along with their local textural features constitutes the recognition pattern to be sent to the supervised neural classifier. The CSS accomplishes the colour region enhancement through a multiple scale loop of oriented filters and competition–cooperation mechanisms. Afterwards, the neural architecture performs an attentive recognition of the scene using those oriented filters responses and the chromatic diffusions. Some comparative tests with other models are included in order to prove the recognition capabilities of this neural architecture and how the use of colour information encourages the texture classification and the accuracy of the boundary detection.     

Retrieval by classification of images containing large manmade objects using perceptual grouping

This paper applies perceptual grouping rules to the retrieval by classification of images containing large manmade objects such as buildings, towers, bridges, and other architectural objects. The semantic interrelationships between primitive image features are exploited by perceptual grouping to extract structure to detect the presence of manmade objects. Segmentation and detailed object representation are not required. The system analyzes each image to extract features that are strong evidence of the presence of these objects. These features are generated by the strong boundaries typical of manmade structures: straight line segments, longer linear lines, coterminations, “L” junctions, “U” junctions, parallel lines, parallel groups, “significant” parallel groups, cotermination graph, and polygons. A K-nearest neighbor framework is employed to classify these features and retrieve the images that contain manmade objects. Results are demonstrated for two databases of monocular outdoor images.     

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。     

基于网格的图像内容检索研究

信息时代的到来,使得信息的涵义大为扩展,图像作为信息的一种,其数量呈现指数增长,而且基于内容的图像检索技术一直存在检索实时性、计算复杂性、动态性强及内容难以精确描述等问题的困扰.针对这些问题,结合知识学习和网格的特点,利用开放网格服务结构核心思想提出了一个基于网格图像内容检索框架,重点设计体系结构及其功能.     

图像检索中的主动学习及其可测量性

主动学习对于复杂、主观、使用少量训练实例的图像检索查询具有非常有效的作用。在图像检索中应用主动学习与支持向量机相结合的方法进行相关反馈,通过两者的互补来有效地提高图像检索的精度。对比了推理算法、简单主动算法以及角度多样性算法3种主动学习算法,并研究了最好的样本选择策略。还讨论了主动学习中概念复杂度的可测量性,并对未来的研究方向提出了建议。相信随着这些可测量性问题被重点提出,主动学习的成果可以被广泛应用。     

彩色图象检索中的模糊直方图技术

在CBIR应用中,经典的颜色直方图方法具有一定的局限性。根据图象相似匹配中的主观感知特点,本文将模糊信息引入图象的颜色分布表示中,讨论几种描述图象中颜色分布的模糊直方图模型及其相应的距离度量。实验表明,这结模糊颜色直方图模型具有较好的图象辨识能力。     

基于内容的智能图像检索系统设计

由于Internet中的图像信息资源是异构的．人们要想从中发现、收集和维护自己需要的信息要花费大量的时间和精力。为了更有效地利用Internet上的图像资源，文章从基于内容的图像检索的现状出发，运用Agent技术，提出了一种基于内容的智能图像检索系统的初步模型，并设计了其中核心部分的基本结构：用户Agent、协调器和信息Agent，从而弥补了现有图像检索系统在自适应用户兴趣和交互式等方面的不足，提高了系统的检索正确率和检索能力。     

Detection of quantization index modulation steganography in G.723.1 bit stream based on quantization index sequence analysis

This paper presents a method to detect the quantization index modulation(QIM) steganography in G.723.1 bit stream.We show that the distribution of each quantization index(codeword) in the quantization index sequence has unbalanced and correlated characteristics.We present the designs of statistical models to extract the quantitative feature vectors of these characteristics.Combining the extracted vectors with the support vector machine,we build the classifier for detecting the QIM steganography in G.723.1 bit stream.The experiment shows that the method has far better performance than the existing blind detection method which extracts the feature vector in an uncompressed domain.The recall and precision of our method are all more than 90% even for a compressed bit stream duration as low as 3.6 s.     

基于特征点匹配和哈希法的图像检索方法

提出一种基于点特征匹配和几何型哈希法的图像检索方法。利用小波变换提取图像的突变点，以点为辜心划定一小块区域，将图像划分成图像块。提取块的低层次特征矢量，将两幅图像之间的匹配转换成图像块之间的匹配。并采用几何型哈希索引方法实现图像的快速检索。实验证明，这种方法能够取得较高的检索精度，且对图像形变以及局部遮挡等都有较好的适应能力。     

基于平均检索精度的图像特征融合方法

在基于内容的图像检索中,不同图像特征反映了图像不同侧面的内在特性,如何有效地组织和利用这些特征从而提高系统的检索性能是一个值得研究的课题．首先提出了特征互补率的定义,通过计算互补矩阵有指导地选择融合特征集．实验结果表明,互补矩阵能够很好地估计特征之间的补充能力．同时提出了基于平均检索精度的特征线性融合方法,并在一个包含12000张异质图像的大型图像库上与当前图像检索中最常用的几种方法进行了对比实验,结果表明这种方法具有更高的精度．