基于内容图像检索的特征性能评价研究

从基于内容图像检索特征来看,各特征反应了图像各侧面内在特性,利用此种技术检索需要多类相似性度量方法,特征与特征之间的相似性度量方式是当前CBIR的核心课题。使用图形特征,在不同相似性的度量方式下,多种特征在图像库上的检索性能,可为CBIR系统设计提供相应的依据,通过本次实验得知,图像特征检索性能不仅与度量方法有关,也与图像库有密切关联。     

基于内容的图像检索介绍

基于内容特征的图象检索（CBIR）是目前国内外研究的一个热点。本文简要介绍了基于内容的图像检索技术的发展过程及主要原理，重点论述了基于内容的图像检索常用关键技术——图像视觉特征的描述和提取。     

遗传算法在基于内容的图像检索中的应用

利用遗传算法对图像的统计特征进行了特征选取,并应用于颅脑CT图像检索中。实验结果表明,该方法能够有效地选取特征,提高检索精度,改善检索效果。     

基于内容的多特征综合图像检索

相关反馈用于基于内容的图像检索是一个研究热点,事实证明多特征的基于综合图像检索优于基于单一特征的检索,文章在Rui的动态调整权值方法的基础上,提出了一些改进思想,克服原来方法的一些不足。     

基于内容的图像检索介绍

基于内容特征的图象检索(CBIR)是目前国内外研究的一个热点。本文简要介绍了基于内容的图像检索技术的发展过程及主要原理,重点论述了基于内容的图像检索常用关键技术——图像视觉特征的描述和提取。     

基于内容的图像检索技术

综述了基于内容的图像检索（CBIR）技术。分别介绍了在象素域和压缩域CBIR的基本方法及已取得的成果,并对其进行了分析和比较,最后提出今后一些主要研究方向。     

基于内容的图像检索的关键技术

基于内容特征的图像检索(CBIR)是目前国内外研究的一个热点.本文简要介绍了基于内容的图像检索技术的发展过程及主要原理,重点论述了基于内容的图像检索常用关键技术--图像视觉特征的描述和提取.     

基于内容的图像检索技术

随着多媒体技术的迅速发展,传统的基于关键字的信息检索技术已逐渐不能满足要求,因此,基于内容的图像检索技术成为当今的一个研究热点。介绍了基于内容图像检索系统的基本组成。综述了基于颜色、纹理、形状、语义等图像检索技术的主要方法。分析和比较了现有的各种图像检索技术的方法。讨论了相关反馈技术、检索性能的评价等CBIR研究中的关键问题。同时指出了CBIR研究中存在的问题,以及未来的发展趋势和研究方向。     

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。     

基于内容的图像检索中的相关反馈算法

近几年,相关反馈算法作为解决图像检索问题行之有效的方法,受到人们越来越多的重视,各种各样的相关反馈算法层出不穷,该文回顾了早期的启发式相关反馈算法和近期的最优相关反馈算法,对现有的相关反馈算法进行了分析、总结和对比,并提出了今后相关反馈算法的发展方向。     

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．     

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.     

基于内容的图像检索技术研究

阐述基于内容的图像检索研究中存在的问题,并介绍其基本原理、检索方法、关键技术以及研究的进展和趋势.     

Approximating Content-Based Object-Level Image Retrieval

Object-level image retrieval is an active area of research. Given an image, a human observer does not see random dots of colors. Rather, he/she observes familiar objects in the image. Therefore, to make image retrieval more user-friendly and more effective and efficient, object-level image retrieval technique is necessary. Unfortunately, images today are mostly represented as 2D arrays of pixels values. The object-level semantics of the images are not captured. Researchers try to overcome this problem by attempting to deduce the object-level semantics through additional information such as the motion vectors in the case of video clips. Some success stories have been reported. However, deducing object-level semantics from still images is still a difficult problem. In this paper, we propose a color-spatial approach to approximate object-level image retrieval. The color and spatial information of the principle components of an object are estimated. The technique involves three steps: the selection of the principle component colors, the analysis of spatial information of the selected colors, and the retrieval process based on the color-spatial information. Two color histograms are used to aid in the process of color selection. After deriving the set of representative colors, spatial knowledge of the selected colors is obtained using a maximum entropy discretization with event covering method. A retrieval process is formulated to make use of the spatial knowledge for retrieving relevant images. A prototype image retrieval tool has been implemented on the Unix system. It is tested on two image database consisting of 260 images and 11,111 images respectively. The results show that the color-spatial approach is able to retrieve similar objects with much better precision than the sole color-based retrieval methods.     

SVM用于基于内容的自然图像分类和检索

在传统的基于内容图像检索的方法中,由于图像的领域较宽,图像的低级视觉特征和高级概念之间存在着较大的语义间隔,导致检索效果不佳．该文认为更有现实意义的做法是,缩窄图像的领域以减小低级特征和高级概念间的语义间隔,并利用机器学习方法自动建立图像类的模型,从而提供用户概念化的图像查询方式．该文以自然图像领域为例,使用支持向量机(SVM)学习自然图像的类别,学习到的模型用于自然图像分类和检索．实验结果表明作者的方法是可行的．     

基于内容的图像检索研究

基于内容的图像检索是图像检索中较高层次的检索,文中对CBIR的各种特征提取方法进行了分析比较,最后指出了当前的研究热点和今后的发展方向。     

Content-Based Image Retrieval Based on ROI Detection and Relevance Feedback

Content-based image retrieval is an important research topic in computer vision. We present a new method that combines region of interest (ROI) detection and relevance feedback. The ROI based approach is more accurate in describing the image content than using global features, and the relevance feedback makes the system to be adaptive to subjective human perception. The feedback information is utilized to discover the subjective ROI perception of a particular user, and it is further employed to recompute the features associated with ROIs with the updated personalized ROI preference. A fast computation technique is proposed to avoid repeating the ROI detection for images in the database. It directly estimates the features of the ROIs, which makes the query process fast and efficient. For illustration of the overall approach, we use the color saliency and wavelet feature saliency to determine the ROIs. Normalized projections are selected to represent the shape features associated with the ROIs. Experimental results show that the proposed system has better performance than the global features based approaches and region based techniques without feedback.