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.     

SemQuery: semantic clustering and querying on heterogeneous features for visual data

The effectiveness of content-based image retrieval can be enhanced using heterogeneous features embedded in the images. However, since the features in texture, color, and shape are generated using different computation methods and thus may require different similarity measurements, the integration of the retrievals on heterogeneous features is a nontrivial task. We present a semantics-based clustering and indexing approach, termed SemQuery, to support visual queries on heterogeneous features of images. Using this approach, the database images are classified based on their heterogeneous features. Each semantic image cluster contains a set of subclusters that are represented by the heterogeneous features that the images contain. An image is included in a semantic cluster if it falls within the scope of all the heterogeneous clusters of the semantic cluster. We also design a neural network model to merge the results of basic queries on individual features. A query processing strategy is then presented to support visual queries on heterogeneous features. An experimental analysis is conducted and presented to demonstrate the effectiveness and efficiency of the proposed approach.     

An Affine-Invariant Tool for Retrieving Images from Homogeneous Databases

In this paper, we examine the complexities involved in retrieving images from a database comprised of objects of very similar appearance. Such an operation requires a process that can discriminate among images at a very fine level, such as distinguishing among various species of fish. Furthermore, incidental environmental factors such as change in viewpoints and slight, nonessential shape deformation must be excluded from the similarity criteria. To this end, we propose a new method for content-based image retrieval and indexing, one that is well suited for discriminating among objects within the same class in a way that is insensitive to incidental environmental changes. The scheme comprises a global alignment and a local matching process. Affine transform is used to model the different viewpoints associated with positioning the camera, while multi-dimensional indexing techniques are used to make the global alignment scheme efficient. A local matching process based on dynamic programming allows the optimal matching of local structures using cost metrics that may ignore nonessential local shape deformation. Results show the method's ability to cancel out visual distortions caused by a changing viewpoint, and its tolerance to noise, occlusion, and slight deformations of the object.     

Web-based image indexing and retrieval in JPEG compressed domain

As the majority of content-based image retrieval systems operate on full images in pixel domain, decompression is a prerequisite for the retrieval of compressed images. To provide a possible on-line indexing and retrieval technique for those jpg image files, we propose a novel pseudo-pixel extraction algorithm to bridge the gap between the existing image indexing technology, developed in the pixel domain, and the fact that an increasing number of images stored on the Web are already compressed by JPEG at the source. Further, we describe our Web-based image retrieval system, WEBimager, by using the proposed algorithm to provide a prototype visual information system toward automatic management, indexing, and retrieval of compressed images available on the Internet. This provides users with efficient tools to search the Web for compressed images and establish a database or a collection of special images to their interests. Experiments using texture- and colour-based indexing techniques support the idea that the proposed algorithm achieves significantly better results in terms of computing cost than their full decompression or partial decompression counterparts. This technology will help control the explosion of media-rich content by offering users a powerful automated image indexing and retrieval tool for compressed images on the Web.J. Jiang: Contacting author     

基于色彩主特征的快速图像检索

提出了一种新颖而又简单直观的图像索引机制。这种方法通过统计图像数据库的低层像素特征,用中值切割法构建索引树,把自然景物图像数据库分成易于描述的不同视觉主题,并籍此来进行高效的图像检索。索引树的构建有别于传统的完全基于数值本身的聚类方法,因而聚类结果视觉含义更明显。本文以颜色主题为例实现了此方法,并通过实验论证了方法的高效和快速。图像索引码以二进制形式给出,因而所需存储空间也极小。     

一种有效的支持海量图像数据库QBE查询的聚类索引算法

对海量图像数据进行基于内容的查询与检索有赖于高效的索引和检索机制。因此,如何将海量图像数据进行合理的分类,人而建立相应的索引机制就成为了一个亟待解决的问题。本文提出了一种有效的支持海量图像数据库QBE查询的聚类索引算法。实验在1万多幅的图像数据库上进行了反复测试,结果表明该算法可以极大地提高检索效率。     

分形图像编码的改进算法

分形图像编码是一种基于自然图像局部自相似性的有效压缩技术。通过引入一个可以影响解码图像质量和编码时间的控制参数，该文提出了分形图像编码的一种改进方案。该方案既不需要复杂的理论分析，也不需要改变现有的分形解码过程，因此能够以直接的方式融入其它的分形图像编码算法。计算机仿真显示，对一组复杂性不同的测试图像，以PSNR(peak signal-to-noise ratio)度量的解码图像质量优于对应的分形图像编码算法的解码图像质量，同时编码时间也大幅度减少。     

Learning Based Neural Similarity Metrics for Multimedia Data Mining

Multimedia data mining refers to pattern discovery, rule extraction and knowledge acquisition from multimedia database. Two typical tasks in multimedia data mining are of visual data classification and clustering in terms of semantics. Usually performance of such classification or clustering systems may not be favorable due to the use of low-level features for image representation, and also some improper similarity metrics for measuring the closeness between multimedia objects as well. This paper considers a problem of modeling similarity for semantic image clustering. A collection of semantic images and feed-forward neural networks are used to approximate a characteristic function of equivalence classes, which is termed as a learning pseudo metric (LPM). Empirical criteria on evaluating the goodness of the LPM are established. A LPM based k-Mean rule is then employed for the semantic image clustering practice, where two impurity indices, classification performance and robustness are used for performance evaluation. An artificial image database with 11 semantics is employed for our simulation studies. Results demonstrate the merits and usefulness of our proposed techniques for multimedia data mining.     

基于迭代分形的图象压缩和检索方法

图象所具有的海量性和无序性的特点，决定了多媒体应用的构建必须解决图象数据的高效压缩和有效检索两个关键问题，而由于传统的压缩和检索技术的研究是相互分离的，因而限制了多媒体应用系统整体性能的提高，针对此问题，从两者相互结合的观点，对图象压缩和检索方法进行了研究，首先在小波变换域内，基于迭代分形对图象数据进行压缩，然后在图象分形码的基础上，利用迭代函数系统分布特性构建的特征量来支持图象检索，实验结果验证了该方法的可行性和有效性，同时也表明了基于迭代分形的图象检索方法所具有的巨大应用潜力。     

Content-based indexing of multimedia databases

Content-based retrieval of multimedia database calls for content-based indexing techniques. Different from conventional databases, where data items are represented by a set of attributes of elementary data types, multimedia objects in multimedia databases are represented by a collection of features; similarity of object contents depends on context and frame of reference; and features of objects are characterized by multimodal feature measures. These lead to great challenges for content-based indexing. On the other hand, there are special requirements on content-based indexing: to support visual browsing, similarity retrieval, and fuzzy retrieval, nodes of the index should represent certain meaningful categories. That is to say that certain semantics must be added when performing indexing. ContIndex, the context-based indexing technique presented in this paper, is proposed to meet these challenges and special requirements. The indexing tree is formally defined by adapting a classification-tree concept. Horizontal links among nodes in the same level enhance the flexibility of the index. A special neural-network model, called Learning based on Experiences and Perspectives (FEP), has been developed to create node categories by fusing multimodal feature measures. It brings into the index the capability of self-organizing nodes with respect to certain context and frames of reference. An icon image is generated for each intermediate node to facilitate visual browsing. Algorithms have been developed to support multimedia object archival and retrieval using Contlndex     

视觉相似性计算的艺术图像自组织方法

为解决大量数字化艺术图像常规组织和管理复杂低效问题,提出一种基于图像相似性计算的自组织方法,对艺术图像提取了颜色、纹理、空间布局和SIFT等用于相似性计算的视觉特征表示,并根据艺术图像空间布局特点设计计算模型,试验了特征的聚类效果。采用多层版本近邻传播聚类（MLAP）算法为基础,对实验图像库进行层次化聚类,构建图像的层次化浏览结构。实验结果表明,该方法在艺术图像的管理和使用上都有着良好的性能。     

映射结合聚类的视频关键帧提取

目的 视频摘要技术在多媒体数据处理和计算机视觉中都扮演着重要的角色。基于聚类的摘要方法多结合图像全局或局部特征,对视频帧进行集群分类操作,再从各类中获取具有代表性的关键帧。然而这些方法多需要提前确定集群的数目,自适应的方法也不能高效的获取聚类的中心。为此,提出一种基于映射和聚类的图像密度值分析的关键帧选取方法。方法 首先利用各图像间存在的差异,提出将其映射至2维空间对应点的度量方法,再依据点对间的相对位置和邻域密度值进行集群的聚类,提出根据聚类的结果从视频中获取具有代表性的关键帧的提取方法。结果 分别使用提出的度量方法对Olivetti人脸库内图像和使用关键帧提取方法对Open Video库进行测试,本文关键帧提取方法的平均查准率达到66%、查全率达到74%,且F值较其他方法高出11%左右达到了69%。结论 本文提出的图像映射后聚类的方法可有效进行图像类别的识别,并可有效地获取视频中的关键帧,进而构成视频的摘要内容。     

集成视觉特征和语义信息的相关反馈方法

为了有效地利用图像检索系统的语义分类信息和视觉特征,提出一种基于Bayes的集成视觉特征和语义信息的相关反馈检索方法.首先,将图像库的数据经语义监督的视觉特征聚类算法划分为小的聚类,每个聚类内数据的视觉特征相似并且语义类别相同;然后以聚类为单位标注正负反馈的实例,这显著区别于以单个图像为单位的相关反馈过程;最后分别以基于视觉特征的Bayes分类器和基于语义的Bayes分类器修正相似距离.在图像库上的实验表明,只用较少的反馈次数就可以达到较高的检索准确率.     

Indexing chromatic and achromatic patterns for content-based colour image retrieval

In this paper, we present a method to represent achromatic and chromatic image signals independently for content-based image indexing and retrieval for image database applications. Starting from an opponent colour representation, human colour vision theories and modern digital signal processing technologies are applied to develop a compact and computationally efficient visual appearance model for coloured image patterns. We use the model to compute the statistics of achromatic and chromatic spatial patterns of colour images for indexing and content-based retrieval. Two types of colour images databases, one colour texture database and another photography colour image database are used to evaluate the performance of the developed method in content-based image indexing and retrieval. Experimental results are presented to show that the new method is superior or competitive to state-of-the-art content-based image indexing and retrieval techniques.     

Image retrieval based on histogram of fractal parameters

Image indexing and retrieval techniques are important for efficient management of visual databases. These techniques are generally developed based on the associated compression techniques. In the fractal domain, luminance offset and contrast scaling parameter are typically used as the fractal indices. However, luminance offset and contrast scaling parameter are strongly correlated. In this paper, we prove that range block mean and contrast scaling parameters are independent. Based on this independence, we propose four statistical indices for efficient image retrieval. In addition, we propose an efficient hierarchical indexing strategy based on the de and ac component analysis. Experimental results on a database of 416 texture images, created by decomposing 26 images, indicate that the proposed indices significantly improve the retrieval rate, compared to other retrieval methods.     

Color indexing for efficient image retrieval

Content based image retrieval is an active area of research. Many approaches have been proposed to retrieve images based on matching of some features derived from the image content. Color is an important feature of image content. The problem with many traditional matching-based retrieval methods is that the search time for retrieving similar images for a given query image increases linearly with the size of the image database. We present an efficient color indexing scheme for similarity-based retrieval which has a search time that increases logarithmically with the database size.In our approach, the color features are extracted automatically using a color clustering algorithm. Then the cluster centroids are used as representatives of the images in 3-dimensional color space and are indexed using a spatial indexing method that usesR-tree. The worst case search time complexity of this approach isOn _q log(N^* n_avg)), whereN is the number of images in the database, andn _q andn _avg are the number of colors in the query image and the average number of colors per image in the database respectively. We present the experimental results for the proposed approach on two databases consisting of 337 Trademark images and 200 Flag images.     

基于FRD的图像纹理情感语义提取

图像的低层视觉特征(颜色、纹理和形状)中包含大量人类可感知的情感语义信息。利用纹理特征,提出一种新的索引方法——模糊认识度(FRD)聚类法,用来描述与情感相关联的语义图像。FRD聚类法能从高层的情感概念出发进行图像检索。索引使用3个感性的纹理特征：方向性,对比度和粗糙度生成FRD值。实验采用室内装饰图片,结果表明,该方法性能较好。     

Content-based Access in Image Database by Quantitative Relationships

In this paper, we describe a novel technique to perform content-based access in image databases using quantitative spatial relationships. Usually, spatial relation-based indexing methods fail if the metric spatial information contained in the images must be preserved. In order to provide a more robust approach to directional relations indexing with respect to metric differences in images, this paper introduces an improvement of the virtual image index, namely quantitative virtual image, using a quantitative methodology. A scalar quantitative measure is associated with each spatial relation, in order to discriminate among images of the image database having the same objects and spatial relationships, but different degree of similarity if we also consider distance relationships. The measure we introduce does not correspond to any significant increase of complexity with respect to the standard virtual image providing a more precise answer set.     

Inference and retrieval of facial images

Attempts have been made to extend SQL to work with multimedia databases. We are reserved on the representation ability of extended SQL to cope with the richness in content of multimedia data. In this paper we present an example of a multimedia database system, Computer Aided Facial Image Inference and Retrieval system (CAFIIR). The system stores and manages facial images and criminal records, providing necessary functions for crime identification. We would like to demonstrate some core techniques for multimedia database with CAFIIR system. Firstly, CAFIIR is a integrated system. Besides database management, there are image analysis, image composition, image aging, and report generation subsystems, providing means for problem solving. Secondly, the richness of multimedia data urges feature-based database for their management. CAFIIR is feature-based. A indexing mechanism,iconic index, has been proposed for indexing facial images using hierarchical self-organization neural network. The indexing method operates on complex feature measures and provides means for visual navigation. Thirdly, special retrieval methods for facial images have been developed, including visual browsing, similarity retrieval, free text retrieval and fuzzy retrieval.     

基于两种纹理特征聚类的图像检索

设计和实现两种不同的分形维数作为纹理特征进行聚类的方法.提取几百幅不同图像的两种纹理特征,对特征库按聚类算法建立索引结构,形成图像的分类库,通过两种不同纹理特征的检索与无聚类的特征检索相比,实验结果表明聚类方法大大缩短了检索时间.结论:作为海量图像的检索,有效的图像特征结合聚类是一个有力工具,在研究信息分类与识别等方面具有应用潜力.