Colour Image Indexing Using SOM for Region-of-Interest Retrieval

In this paper, we present a novel approach to image indexing by incorporating a neural network model, Kohonen’s Self-Organising Map (SOM), for content-based image retrieval. The motivation stems from the idea of finding images by regarding users’ specifications or requirements imposed on the query, which has been ignored in most existing image retrieval systems. An important and unique aspect of our interactive scheme is to allow the user to select a Region-Of-Interest (ROI) from the sample image, and subsequent query concentrates on matching the regional colour features to find images containing similar regions as indicated by the user. The SOM algorithm is capable of adaptively partitioning each image into several homogeneous regions for representing and indexing the image. This is achieved by unsupervised clustering and classification of pixel-level features, called Local Neighbourhood Histograms (LNH), without a priori knowledge about the data distribution in the feature space. The indexes generated from the resultant prototypes of SOM learning demonstrate fairly good performance over an experimental image database, and therefore suggest the effectiveness and significant potential of our proposed indexing and retrieval strategy for application to content-based image retrieval. Receiveed: 4 June 1998?,Received in revised form: 7 January 1999?Accepted: 7 January 1999     

一种基于轮廓特征点的图像检索方法

传统基于形状的图像检索方法检索效率较低,针对该问题,提出一种基于对象轮廓特征点的图像检索方法。利用Mean Shift算法提取感兴趣对象,以对象曲率的局部极值点作为特征点,并将对象表示为这些特征点的特征向量,定义检索对象与被检索对象特征向量间的距离匹配机制,实现对象的匹配或识别。实验结果表明,与传统方法相比,该方法具有较高的查全率和查准率。     

基于特征融合的WCE图像检索

无线胶囊内镜(WCE)是一种新型的、具有极大市场价值的肠道疾病检测技术。该技术产生的图像数据量极大,为医生开展疾病诊断带来了困难。WCE图像快速检索技术是WCE后端智能图像分析软件的基本功能之一。本文针对WCE图像中相似图像的快速检索问题开展研究。通过对大量WCE图像本身特征的分析,提出了一种融合颜色特征与纹理特征的图像检索算法。首先分别提取图像的色调饱和度二维相关直方图以及图像的局部二值模式(LBP)纹理特征,然后采用高斯归一化的方法对不同的特征进行融合,最后采用融合特征为待检索图像建立索引,实现了输入目标WCE图像的快速准确查找。实验结果表明本文所提方法计算复杂度低和检索结果良好。     

基于特征点匹配技术的图像检索方法

图像数据的无序激增使得基于内容的图像检索技术成为一个研究热点。为了克服普通颜色特征不考虑颜色的空间分布的缺点,提出了一种多分辨率分块特征点匹配的图像检索技术。同时,还将相关反馈技术融合到多分辨率分块主色算法中以便改善检索效果。实验表明,文中所叙述的方法是比较有效的。     

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。     

采用形态学边界特征的医学图像检索

特征提取是基于内容的图像检索(CBIR)中的关键步骤,如何有效提取反映高层语义的图像特征对于医学图像的检索至关重要.提出一种基于边界形状特征的医学图像检索方法.该方法首先通过多尺度形态学方法检测图像边界点,然后对边界图像进行形状特征提取,构建边界的形状密度直方图,最后通过相似性匹配实现医学图像检索.实验结果证明了所提取的边界形状特征在医学图像检索中的有效性,通过对比实验给出了结果分析和进一步的研究思路.     

综合颜色和局部空间特征的彩色图像检索方法

针对图像颜色空间的分布特性,提出一种用颜色色调直方图来表达图像的颜色特征;通过HSV颜色空间的三个独立分量表示方法来描述颜色的空间特征,具有旋转、平移、尺度不变特性.实验表明,该方法很好地克服了颜色直方图和颜色矩无法表达图像色彩的空间位置的缺点,显示了组合特征的图像检索特征的有效性和较高的检索准确性.     

综合颜色和局部空间特征的彩色图像检索方法

针对图像颜色空间的分布特性,提出一种用颜色色调直方图来表达图像的颜色特征;通过HSV颜色空间的三个独立分量表示方法来描述颜色的空间特征,具有旋转、平移、尺度不变特性。实验表明,该方法很好地克服了颜色直方图和颜色矩无法表达图像色彩的空间位置的缺点,显示了组合特征的图像检索特征的有效性和较高的检索准确性。     

基于目标区域的图像综合特征检索

提出了一种基于目标区域的图像综合特征检索方法。根据颜色、纹理特征的综合向量采用改进的K均值聚类算法把每幅图像分成若干个区域,并且利用综合特征表示和检索分割后的区域,该方法在区域匹配时考虑了不同特征在重要程度上的差别。为了改善检索性能,系统中引入了快速索引和相关反馈技术。     

Region-based Image Retrieval Using Probabilistic Feature Relevance Learning

Region-Based Image Retrieval (RBIR), a specialisation of content-based image retrieval, is a promising and important research area. RBIR usually requires good segmentation, which is often difficult to achieve in practice for several reasons, such as varying environmental conditions and occlusion. It is, therefore, imperative to develop effective mechanisms for interactive, region-based visual query in order to provide confident retrieval performance. In this paper, we present a novel RBIR system, Finding Region In the Pictures (FRIP), that uses human-centric relevance feedback to create similarity metric on-the-fly in order to overcome some of the limitations associated with RBIR systems. We use features such as colour, texture, normalised area, shape and location, extracted from each region of a segmented image, to represent image content. For each given query, we estimate local feature relevance using probabilistic relevance model, from which to create a flexible metric that is highly adaptive to query location. As a result, local data densities can be sufficiently exploited, whereby rapid performance improvement can be achieved. The efficacy of our method is validated and compared against other competing techniques using real world image data.     

基于轮廓重构和特征点弦长的图像检索

轮廓描述法作为形状检索中最为关键的步骤,应体现目标的整体形状信息和重要特征点信息,并具备对噪声干扰的鲁棒性.提出一种基于轮廓重构和特征点弦长的图像检索算法,首先在目标轮廓提取的基础上分析轮廓的能量保持率,并进行轮廓的降维重构处理,从而减少了随机噪声造成的轮廓畸变.然后,通过新定义的支持域来计算轮廓点的特征强度,并分析了支持域半径与特征点提取结果的关系,从而筛选出有效的轮廓特征点.最后,根据轮廓点和相应特征点间的弦长关系构造轮廓特征函数,经相应处理后,最终得到的形状描述子满足不变性要求.大量实验结果表明,该算法无论是在常规样本库中,还是在噪声样本库中都具有更优的检索性能.     

基于内容图像检索中的索引技术*

首先总结了基于内容图像检索中索引技术的研究现状,指出了其中存在的问题以及今后发展趋势,然后提出了一个新的聚类算法和降维算法,并将两者结合起来提出了一个可用于基于内容图像检索的索引机制。     

应用粗糙集理论实现基于对象的图像检索

粗糙集理论已经成功应用于许多领域,特别是数据挖掘、知识发现、人工智能和信息系统分析。在粗糙集理论背景下．对图像进行建模,用集合的上下近似表示图像对象。通过定义新的图’像相似度计算“Object Similarity Ratio”,介绍一种精确的基于对象的图像检索方法,它可以处理基于例图的查询,并且介绍了一种高效的大型图像库检索算法。     

综合contourlet变换和颜色特征的图像检索算法

针对单一特征图像检索效率不高的缺点,提出一种基于轮廓波变换(contourlet)和颜色直方图的多特征检索方法.Con-tourlet变换采用"双重滤波器组"结构,具备多分辨率、多方向性和各向异性等性质,能以接近最优的方式表示图像的边缘.颜色直方图计算简单,具有旋转、平移的不变性.本算法充分利用Contourlet变换后系数分布特点和HSV空间颜色直方图的旋转平移不变性,构造图像的特征向量,以Euclidean distance作为相似度量标准进行图像检索.实验结果表明,提出的算法具有更高的查准率.     

采用对象特征向量表示法的标签聚类算法

在社会化标记系统中,常采用聚类等数据挖掘技术来解决标签冗余和语意模糊的问题.现有标签聚类算法大多根据不同标签在对象中共同出现的次数来计算它们之间的相似度,但是这种方法聚类的精确度与召回率并不高.针对此问题,提出一种新的标签聚类算法,充分考虑标签的标记信息,采用基于对象的特征向量来精确地表征一个标签,根据余弦相似度公式得到较为准确的标签相似度,然后采用K-Means算法将用户标签进行聚类.实验结果表明该算法能够得到更加精确的聚类结果.     

图像检索中的相似性判别及索引方法综述

基于内容的图像检索(CBIR)是对传统信息检索领域的扩展.它采用图像视觉内容的相似性判别进行查询.CBIR涉及到很多科学领域的课题.本文则仅主要综述CBIR技术中的相似性度量方法,索引方式,以及检索性能的评价.最后,分析了该领域现存的问题、最新研究动态及发展方向.     

基于LSH索引的快速图像检索

高维空间中点数据的索引及检索是基于内容图像检索领域的关键问题,文中将LSH(localitysensitivehashing)索引算法应用于基于内容图像检索系统中,与传统的索引方法相比,该算法具有复杂度比较低、支持非常高的维数、极低的I/O代价等特点。实验结果证明,将该索引算法应用于基于内容图像检索系统中,其性能优于传统的索引方法.     

一种自适应特征分块加权的图像检索方法

文中提出了一种自适应特征加权的图像检索方法。首先提取图像的颜色直方图以及梯度直方图,根据图像的特点,用不同的方法进行分割。然后将已分割图像分成4×4小块,按照其颜色和纹理的分布情况,计算每小块的特征加权值。最后,以分块加权的一维颜色矢量直方图为特征,进行相似度匹配,欧氏距离较小的图像为检索结果。实验结果表明,综合利用颜色和边缘特征比单独用一个特征进行图像检索的效果要好,自适应特征加权后的检索准确率更高,对于拥有较明显的目标与背景差异的图像,该方法特别有效。     

Towards Data-Adaptive and User-Adaptive Image Retrieval by Peer Indexing

Adaptation to the characteristics of specific images and the preferences of individual users is critical to the success of an image retrieval system but insufficiently addressed by the existing approaches. In this paper, we propose an elegant and effective approach to data-adaptive and user-adaptive image retrieval based on the idea of peer indexing—describing an image through semantically relevant peer images. Specifically, we associate each image with a two-level peer index that models the “data characteristics” of the image as well as the “user characteristics” of individual users with respect to this image. Based on two-level image peer indexes, a set of retrieval parameters including query vectors and similarity metric are optimized towards both data and user characteristics by applying the pseudo feedback strategy. A cooperative framework is proposed under which peer indexes and image visual features are integrated to facilitate data- and user-adaptive image retrieval. Simulation experiments conducted on real-world images have verified the effectiveness of our approach in a relatively restricted setting.     

RETIN: A Content-Based Image Indexing and Retrieval System

This paper presents RETIN, a new system for automatic image indexing and interactive content-based image retrieval. The most original aspect of our work rests on the distance computation and its adjustment by relevance feedback. First, during an offline stage, the indexes are computed from attribute vectors associated with image pixels. The feature spaces are partitioned through an unsupervised classification, and then, thanks to these partitions, statistical distributions are processed for each image. During the online use of the system, the user makes an iconic request, i.e. he brings an example of the type of image he is looking for. The query may be global or partial, since the user can reduce his request to a region of interest. The comparison between the query distribution and that of every image in the collection is carried out by using a weighted dissimilarity function which manages the use of several attributes. The results of the search are then refined by means of relevance feedback, which tunes the weights of the dissimilarity measure via user interaction. Experiments are then performed on large databases and statistical quality assessment shows the good properties of RETIN for digital image retrieval. The evaluation also shows that relevance feedback brings flexibility and robustness to the search.