一种结合分形编码的图像检索算法

为了更有效、更准确地进行的图像检索,在迭代函数系统收敛速度理论和拼贴定理的基础上,提出了一种结合分形编码进行基于内容的图像检索方法,即把查询图像的分形码应用于图像库中的图像进行分形迭代,然后将得到的图像与查询图像进行相似匹配,在检索正确率和检索速度方面,优于实验中其他方法。实验证明了该算法的有效性和可行性。     

一种基于熵及分形编码的图像检索方法

图像的抽象描述和特征提取是基于内容的图像检索系统中需要解决的关键问题,提出了一种图像熵和分形编码相结合的图像检索方法。首先,计算图像熵和比较设定的阈值对图像库进行预分类;其次,利用Jacquin方法计算得到查询图像的分形IFS编码,把图像库同类图像作为初始图像进行分形迭代解码;最后,计算解码图像与查询图像的相似距离得到检索结果。实验结果表明,与直接像素值相似匹配方法相比,在基本保证图像检索效率的前提下,极大地提高了检索时间,该算法具有很好的有效性和可行性。     

基于迭代函数系统分形码的图像检索技术

在压缩域对图像检索技术进行了研究,首先对图像库中每幅图像采用分形压缩编码,获得其IFS分形码,然后利用分形码的分布特点计算检索图像与图像库中图像间的距离。实验结果表明,本文所提出算法具有稳定性与有效性。     

基于分形特征的二值图像检索方法的研究

论述了基于分形特征的二值图像检索方法。图像的内容由4种特征来描述:图像分形维数、图像分形矢量、边界分形矢量和骨架分形矢量。实验表明该方法计算简单、有效,匹配快速,检索结果比较理想,只要查询图像在图库中,就一定能通过该方法检索出来。另外,该方法还具有一定的鲁棒性,证明这种检索方法是具有较大实用意义的。     

分形技术图像比对搜索模型

传统的搜索引擎只能搜索文字型的资料，显然无法满足用户想通过搜索引擎来取得与图像相关的信息的需求．提出一种图像比对搜索引擎，利用分形图像处理和索引技术来建立图像特征数据库．当用户输入查询图像时，系统对于用户输入的图像也采用与分形图像处理相同的方式取得特征值，然后再与图像特征数据库的特征矢量作比对，达到数据搜索的目的．实验表明，图像比对搜索引擎除了可以找出用户输入的相似图像外，对于查询图像的旋转、模糊或噪声，图像比对搜索引擎也能够找出正确的图像，证明文中方法对图像的容错性和适应性好。     

多媒体数据库索引技术的研究与实现

现有的数据库索引技术,普遍不能适应多维空间属性的搜索,特别是无法对大容量的多媒体数据进行基于内容的检索。文章分析研究了多媒体数据库的索引结构和索引算法,设计了一种用于大容量图像数据库的索引方法。实验在1万多幅的图像库上反复进行,结论证明该算法能够有效地支持大容量图像库的基于内容检索。     

基于小波的压缩图像索引技术

在JPEG2000压缩框架下给出了两种压缩图像索引方法, 不需要完全解压缩, 减少了数据处理量。实验结果证明，给出的索引方法具有很强的图像表征能力，利用该索引进行图像检索,提高了检索效率。     

基于倒排索引的密文数据库检索方法研究

针对实际密文数据库的应用,在全文检索倒排索引技术的基础上,设计了一种通过密文倒排索引文件对其进行快速检索的方法。密文索引文件中主要包含有索引项、相对应的记录主键等信息。检索时,通过用检索词匹配索引文件中的索引项,找到对应的记录主键集合,再根据记录主键集合查询密文数据库,获取相应的密文数据,进行解密即可获取明文数据信息。整个检索过程中不对数据库进行解密,从而实现了在不解密的情况下对密文数据库的快速检索。     

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。     

数理统计特征的快速图像分形压缩算法研究

图像自相似是图像分形研究中一个重要的研究方向,尤其是对自相似图像子块的特征提取量化问题尤为引人关注。在分形图像编码发展的过程中,图像自相似特征的提取和量化得到了广泛的研究和应用。通过对图像数理统计特征的研究,提出了一种对图像子块进行分类的方法,使拥有相似特征的图像子块能够划分到更小的区域范围内,改进了分形图像压缩算法。经过分析和实验证明,该方法在不影响重建图像质量的前提下,提高了分形编码的速度,较大程度地减少了计算量。     

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.     

Utilizing similarity relationships among existing data for high accuracy processing of content-based image retrieval

Retrieving similar images based on its visual content is an important yet difficult problem. We propose in this paper a new method to improve the accuracy of content-based image retrieval systems. Typically, given a query image, existing retrieval methods return a ranked list based on the similarity scores between the query and individual images in the database. Our method goes further by relying on an analysis of the underlying connections among individual images in the database to improve this list. Initially, we consider each image in the database as a query and use an existing baseline method to search for its likely similar images. Then, the database is modeled as a graph where images are nodes and connections among possibly similar images are edges. Next, we introduce an algorithm to split this graph into stronger subgraphs, based on our notion of graph’s strength, so that images in each subgraph are expected to be truly similar to each other. We create for each subgraph a structure called integrated image which contains the visual features of all images in the subgraph. At query time, we compute the similarity scores not only between the query and individual database images but also between the query and the integrated images. The final similarity score of a database image is computed based on both its individual score and the score of the integrated image that it belongs to. This leads effectively to a re-ranking of the retrieved images. We evaluate our method on a common image retrieval benchmark and demonstrate a significant improvement over the traditional bag-of-words retrieval model.     

Expert system for color image retrieval

Recently, as Web and various databases contain a large number of images, content-based image retrieval (CBIR) applications are greatly needed. This paper proposes a new image retrieval system using color-spatial information from those applications.First, this paper suggests two kinds of indexing keys to prune away irrelevant images to a given query image: major colors' set (MCS) signature related with color information and distribution block signature (DBS) related with spatial information. After successively applying these filters to a large database, we get only small amount of high potential candidates that are somewhat similar to a query image. Then we make use of the quad modeling (QM) method to set the initial weights of two-dimensional cell in a query image according to each major color. Finally, we retrieve more similar images from the database by comparing a query image with candidate images through a similarity measuring function associated with the weights. In that procedure, we use a new relevance feedback mechanism. This feedback enhances the retrieval effectiveness by dynamically modulating the weights of color-spatial information. Experiments show that the proposed system is not only efficient but also effective.     

一种基于SVM的相关反馈图像检索算法

相关反馈技术是近年来图像检索中的研究热点,本文以MPEG-7的边缘直方图作为图像特征,以支持向量机（SVM）为分类器,提出一种新的相关反馈算法。在每次反馈中对用户标记的相关样本进行学习,用历次返回的结果更新训练样本集,建立SVM分类器模型,并根据模型进行检索。本文还对不同核函数的SVM进行了对比,得出RBF核函数的SVM有较高的检索精度。使用由10000幅图像组成的图像库进行实验,结果表明,该算法可有效地检索出更多的相关图像,并且在有限训练样本情况下具有良好的泛化能力。     

Graph-based representation for similarity retrieval of symbolic images

Image retrieval from an image database by the image objects and their spatial relationships has emerged as an important research subject in these decades. To retrieve images similar to a given query image, retrieval methods must assess the similarity degree between a database image and the query image by the extracted features with acceptable efficiency and effectiveness. This paper proposes a graph-based model SRG (spatial relation graph) to represent the semantic information of the contained objects and their spatial relationships in an image with no file annotation. In an SRG graph, the image objects are symbolized by the predefined class names as vertices and the spatial relations between object pairs are represented as arcs. The proposed model assesses the similarity degree between two images by calculating the maximum common subgraph of two corresponding SRG’s through intersection, which has quadratic time complexity owing to the characteristics of SRG. Its efficiency remains quadratic regardless of the duplication rate of the object symbols. The extended model SRG_T is also proposed, with the same time complexity, for the applications that need to consider the topological relations among objects. A synthetic symbolic image database and an existing image dataset are used in the conducted experiments to verify the performance of the proposed models. The experimental results show that the proposed models have compatible retrieval quality with remarkable efficiency improvements compared with three well-known methods LCS_Clique, SIM_R, and 2D Be-string, where LCS_Clique utilizes the number of objects in the maximum common subimage as its similarity function, SIM_R uses accumulation-based similarity function of similar object pairs, and 2D Be-string calculates the similarity of 2D patterns by the linear combination of two 1D similarities.     

A content based image retrieval system for a biological specimen collection

Digital photography and decreasing cost of storing data in digital form has led to an explosion of large digital image repositories. Since the number of images in image databases can be large (millions in some cases) it is important to develop automated tools to search them. In this paper, we present a content based image retrieval system for a database of parasite specimen images. Unlike most content based image retrieval systems, where the database consists of objects that vary widely in shape and size, the objects in our database are fairly uniform. These objects are characterized by flexible body shapes, but with fairly rigid ends. We define such shapes to be FleBoRE (Flexible Body Rigid Extremities) objects, and present a shape model for this class of objects. We have defined similarity functions to compute the degree of likeness between two FleBoRE objects and developed automated methods to extract them from specimen images. The system has been tested with a collection of parasite images from the Harold W. Manter Laboratory for Parasitology. Empirical and expert-based evaluations show that query by shape approach is effective in retrieving specimens of the same class.