结合流形排序和区域匹配的图像检索

给出一种基于数据流形排序(Manifold Ranking)和分割区域匹配的图像检索方法.在Manifold Ranking方法的基础上,提出区域匹配图(Region Matching Graph,RMG)的方法,通过计算图像的区域匹配权值,进行第二次相似性匹配,提高了匹配准确性.在Corel图像数据库对该方法进行了检索仿真,结果表明该方法能有效提高检索的准确性.     

多纹理自然图像检索技术的研究

自然图像一般都是多纹理图像.目前,对于多纹理图像的检索通常是基于纹理分割的结果进行的.纹理分割的局限性常给检索带来负面影响.由于用户通常只对查询的自然图像某个部分感兴趣,这个局部区域的纹理一般相对单一.根据这个查询特点,基于聚类空间模型度量纹理相似度,以部分匹配的方式,进行无需分割的自然图像检索,从而避免了不恰当的纹理分割带来的检索误差.     

基于均匀区域分割的图像检索研究

为减少图像分割准确性对基于内容的图像检索效率的影响,提出了一种基于均匀区域分割的图像检索算法。首先对图像进行均匀区域划分,提取其区域直方图颜色特征和Gabor小波纹理特征,再利用与所提取的图像特征相适应的相似性度量实现有效检索。实验结果表明,与SIMPLIcity系统相比,该算法平均检索性能提高了3.6%,具有良好的平均查找率。     

一种用于图像检索的DP匹配方法

相似性测量是图像检索中的一个重要步骤,距离度量是相似性测量的一个重要方法,DP匹配是距离度量的一种特殊形式。该文在分析一维DP匹配的基本原理后,提出用能量矩阵代替DP匹配中的距离矩阵。能量矩阵是根据图像分割区域的边界点的能量来定义的。图像检索实验结果表明,改进的DP匹配方法在图像检索中的应用是可行的,且有一定的优越性。     

一种基于用户感兴趣区域的图像检索算法

利用图像全局特征的检索不能很好地检索用户想要的对象内容，而基于分割后各区域特征的检索又过分依赖于复杂的图像分割算法。针对上述两者的缺点，文中提出了一种基于用户感兴趣区域的图像检索算法。该算法首先对样例进行多分辨率树状分解，再由用户选择分解后的任意多个感兴趣的子图，提取子图的特征以进行相似性度量，并应用相关反馈以更好地捕获用户的检索意图。该方法无需对图像进行复杂的分割就能提取对象特征，且经由实验证明具有较高的查全率。     

一种基于区域综合特征的图像检索算法

针对基于内容的图像检索所面临的图像低级视觉特征和高级语义之间的语义鸿沟问题,提出一种基于区域的图像检索算法。在LUV颜色空间中使用K均值聚类算法进行图像分割,提取分割后各区域的颜色、形状和区域自相关特征构成区域的综合特征,采用二次型距离相似性度量方法完成图像之间相似性的计算。实验结果表明,该算法具有较好的图像检索性能,与MIRROR中各算法相比,使用平均归一化修正检索等级得到的检索性能提高了12%~47.8%。     

一种基于前景与背景划分的区域图像检索方法及实现

区域图像检索(RBIR)是基于内容图像检索(CBIR)的一个分支,它以图像分割为基础,通过图像局部视觉特征的相似性进行图像检索。由于准确的图像分割技术尚不成熟,区域图像检索性能容易受到冗余分割和错误分割的影响。为了降低RBIR中图像分割的影响,提出了一种基于前景和背景划分的区域图像检索方法。该方法通过规则分块、图像分类和有效区域定位来得到图像分割区域,然后应用中心对象提取算法(COEA)获得图像主体对象,最后提取颜色和纹理特征进行相似度匹配。实现了一个基于上述方法的RBIR系统ObFind,实验结果表明该方法不仅具有与SIMPLIcity相当的检索性能,而且计算复杂度更低。     

一个基于语义的图像检索系统:VisEngine

介绍了一个基于语义的图像检索系统——VisEngine，该系统采用基于图像主要区域的图像分割方法，分别提取图像前景、背景和全局的视觉和抽象语义内容，构造相应的语义模板。接着把这些特征导入到一个面向对象的中间信息结构中，在此基础上进行多种方式的相似性匹配和检索。系统支持多种查询方式，用户交互界面自然友好。实验表明，VisEngine系统能有效地提高首次用户查询的正确性。     

一种基于用户感兴趣区域的图像检索算法

利用图像全局特征的检索不能很好地检索用户想要的对象内容,而基于分割后各区域特征的检索又过分依赖于复杂的图像分割算法。针对上述两者的缺点,文中提出了一种基于用户感兴趣区域的图像检索算法。该算法首先对样例进行多分辨率树状分解,再由用户选择分解后的任意多个感兴趣的子图,提取子图的特征以进行相似性度量,并应用相关反馈以更好地捕获用户的检索意图。该方法无需对图像进行复杂的分割就能提取对象特征,且经由实验证明具有较高的查全率。     

一种基于模板匹配的芯片Frame图像分割算法

针对背景复杂、边界模糊以及芯片相连等特征的芯片Frame图像,提出了一种基于模板匹配的芯片Frame图像分割算法。首先,对整幅芯片Frame图像预分割出多个区域模块;然后,基于区域模块图像采取模板匹配算法匹配单芯片图像;最后,通过合并单芯片的重叠匹配框并记录合并框的坐标信息的方式分割出单芯片图像。实验结果表明:选取合适的模板和阈值,能使该算法的分割准确率达到100%,并且比不基于区域模块匹配的分割算法节省了至少45.76%的分割时间,满足芯片Frame高精度和高速度的分割需求,为芯片图像分割算法的研究提供了一种新思路。     

Content-based image retrieval using growing hierarchical self-organizing quadtree map

In this paper, a growing hierarchical self-organizing quadtree map (GHSOQM) is proposed and used for a content-based image retrieval (CBIR) system. The incorporation of GHSOQM in a CBIR system organizes images in a hierarchical structure. The retrieval time by GHSOQM is less than that by using direct image comparison using a flat structure. Furthermore, the ability of incremental learning enables GHSOQM to be a prospective neural-network-based approach for CBIR systems. We also propose feature matrices, image distance and relevance feedback for region-based images in the GHSOQM-based CBIR system. Experimental results strongly demonstrate the effectiveness of the proposed system.     

Collaborative image retrieval via regularized metric learning

In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between the low-level image features used for computing image similarity and the high-level semantic concepts conveyed in images. One way to reduce the semantic gap is to utilize the log data of users' feedback that has been collected by CBIR systems in history, which is also called “collaborative image retrieval.” In this paper, we present a novel metric learning approach, named “regularized metric learning,” for collaborative image retrieval, which learns a distance metric by exploring the correlation between low-level image features and the log data of users' relevance judgments. Compared to the previous research, a regularization mechanism is used in our algorithm to effectively prevent overfitting. Meanwhile, we formulate the proposed learning algorithm into a semidefinite programming problem, which can be solved very efficiently by existing software packages and is scalable to the size of log data. An extensive set of experiments has been conducted to show that the new algorithm can substantially improve the retrieval accuracy of a baseline CBIR system using Euclidean distance metric, even with a modest amount of log data. The experiment also indicates that the new algorithm is more effective and more efficient than two alternative algorithms, which exploit log data for image retrieval.     

Content-based image retrieval by using tree-structured features and multi-layer self-organizing map

A new approach for content-based image retrieval (CBIR) is described. In this study, a tree-structured image representation together with a multi-layer self-organizing map (MLSOM) is proposed for efficient image retrieval. In the proposed tree-structured image representation, a root node contains the global features, while child nodes contain the local region-based features. This approach hierarchically integrates more information of image contents to achieve better retrieval accuracy compared with global and region features individually. MLSOM in the proposed method provides effective compression and organization of tree-structured image data. This enables the retrieval system to operate at a much faster rate than that of directly comparing query images with all images in databases. The proposed method also adopts a relevance feedback scheme to improve the retrieval accuracy by a respectable level. Our obtained results indicate that the proposed image retrieval system is robust against different types of image alterations. Comparative results corroborate that the proposed CBIR system is promising in terms of accuracy, speed and robustness.     

FAST: Toward more effective and efficient image retrieval

This paper focuses on developing a Fast And Semantics-Tailored (FAST) image retrieval methodology. Specifically, the contributions of FAST methodology to the CBIR literature include: (1) development of a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into a region-based approach to improving the retrieval effectiveness and robustness; (2) development of a new hierarchical indexing structure and the corresponding hierarchical, elimination-based A* retrieval (HEAR) algorithm to significantly improve the retrieval efficiency without sacrificing the retrieval effectiveness; it is shown that HEAR is guaranteed to deliver a logarithm search in the average case; (3) employment of user relevance feedback to tailor the effective retrieval to each user's individualized query preference through the novel indexing tree pruning (ITP) and adaptive region weight updating (ARWU) algorithms. Theoretical analysis and experimental evaluations show that FAST methodology holds great promise in delivering fast and semantics-tailored image retrieval in CBIR.     

Relevance Feedback and Learning in Content-Based Image Search

A major bottleneck in content-based image retrieval (CBIR) systems or search engines is the large gap between low-level image features used to index images and high-level semantic contents of images. One solution to this bottleneck is to apply relevance feedback to refine the query or similarity measures in image search process. In this paper, we first address the key issues involved in relevance feedback of CBIR systems and present a brief overview of a set of commonly used relevance feedback algorithms. Almost all of the previously proposed methods fall well into such framework. We present a framework of relevance feedback and semantic learning in CBIR. In this framework, low-level features and keyword annotations are integrated in image retrieval and in feedback processes to improve the retrieval performance. We have also extended framework to a content-based web image search engine in which hosting web pages are used to collect relevant annotations for images and users' feedback logs are used to refine annotations. A prototype system has developed to evaluate our proposed schemes, and our experimental results indicated that our approach outperforms traditional CBIR system and relevance feedback approaches.     

Content based image retrieval systems

Images are being generated at an ever-increasing rate by sources such as defence and civilian satellites, military reconnaissance and surveillance flights, fingerprinting and mug-shot-capturing devices, scientific experiments, biomedical imaging, and home entertainment systems. For example, NASA's Earth Observing System will generate about 1 terabyte of image data per day when fully operational. A content-based image retrieval (CBIR) system is required to effectively and efficiently use information from these image repositories. Such a system helps users (even those unfamiliar with the database) retrieve relevant images based on their contents. Application areas in which CBIR is a principal activity are numerous and diverse. With the recent interest in multimedia systems, CBIR has attracted the attention of researchers across several disciplines     

一种基于目标区域的图像检索方法

为了弥补颜色直方图等全局特征在描述彩色图像空间信息上的不足,该文提出了一种基于目标区域的彩色图像检索方法。该方法首先利用一种基于颜色视觉一致性的图像分割方法提取出有意义的目标区域,然后分别对各个区域提取HSV彩色直方图和Hu不变矩作为目标区域的特征描述,最后提出了一种相应的计算相似度的方法,实现了图像之间的相似度度量。通过在中科院计算所的Mires图像数据库和ViViLab测试图像库上进行实验,该文提出的方法对于目标明确、背景不太复杂的图像可以达到较好的检索效果。     

Automatic localization and annotation of facial features using machine learning techniques

Content-based image retrieval (CBIR) systems traditionally find images within a database that are similar to query image using low level features, such as colour histograms. However, this requires a user to provide an image to the system. It is easier for a user to query the CBIR system using search terms which requires the image content to be described by semantic labels. However, finding a relationship between the image features and semantic labels is a challenging problem to solve. This paper aims to discover semantic labels for facial features for use in a face image retrieval system. Face image retrieval traditionally uses global face-image information to determine similarity between images. However little has been done in the field of face image retrieval to use local face-features and semantic labelling. Our work aims to develop a clustering method for the discovery of semantic labels of face-features. We also present a machine learning based face-feature localization mechanism which we show has promise in providing accurate localization.