用于图像检索的MPEG-7形状描述子

介绍了基于内容的图像检索技术的研究现状，并对将MPEG-7形状描述子应用于图像数据库进行图像检索进行了研究，最后提出了一些需要解决的问题和今后的研究方向。     

基于内容的图像检索技术

目前各行业对图像的使用越来越广泛,如何有效、快速地从大规模图像数据库中检索出需要的图像,是目前一个相当重要而又富有挑战性的研究课题.但传统的图像检索技术是基于文本的检索技术,这种方法虽然简单易行,但存在一些致命的缺点,严重影响了对图像信息的有效使用.为了克服传统方法的缺点,提出了基于内容的图像检索技术,该技术能够全面客观地提取图像内容,能有效地获取所需的视觉信息,能使图像数据库中的信息得到有效的管理.     

基于内容的图像检索

主要围绕基于内容的图像检索的相关技术进行了研究。设计分析了基于内容的图像检索系统的具体结构,研究了结构中各模块的具体内容。并且用Matlab软件工具基本实现了基于内容的图像检索,以手机图像为例,验证了算法的正确性。     

一种有效的基于内容的图像检索方法

本文针对基于内容的图像检索中特征和相似度问题，提出新的距离模式，并以彩色空间中扩展共发矩阵作为纹理描述，在测试系统iPhoto上，数据库为56600幅图像时，实验结果显示，本文方法优于传统方法。     

A new texture image retrieval way

This paper proposes a new texture image retrieval method for the considering of the population search and random information exchange merits of evolving programming which can be used to optimize image feature vector extraction. The experimental results show that this way can efficiently improve the retrieval accuracy and realize fasttips with the advantage of evolving programming algorithm.     

一种分形域基于内容的图像检索方法

基于内容的图像检索是多媒体、网络通信及计算机等应用研究领域的一项关键技术。该文提出了一种在分形压缩域直接进行基于内容的图像检索方法。该方法不需要对查询图像进行分形变换，因此可以提高检索速度，降低检索复杂度。仿真结果表明，使用该文提出的方法，能够有效地进行分形域基于内容的图像检索，比较大幅度地降低了检索时间，优于试验中其他3种方法。     

Fusing semantic aspects for image annotation and retrieval

In this paper, we present an approach based on probabilistic latent semantic analysis (PLSA) to achieve the task of automatic image annotation and retrieval. In order to model training data precisely, each image is represented as a bag of visual words. Then a probabilistic framework is designed to capture semantic aspects from visual and textual modalities, respectively. Furthermore, an adaptive asymmetric learning algorithm is proposed to fuse these aspects. For each image document, the aspect distributions of different modalities are fused by multiplying different weights, which are determined by the visual representations of images. Consequently, the probabilistic framework can predict semantic annotation precisely for unseen images because it associates visual and textual modalities properly. We compare our approach with several state-of-the-art approaches on a standard Corel dataset. The experimental results show that our approach performs more effectively and accurately.     

图像情感检索研究的进展与展望

基于语义内容的图像检索是解决图像简单视觉特征和用户检索丰富语义之间存在的语义鸿沟的关键。其中情感语义是最高层的语义。本文首先介绍情感图像检索的一般框架，并引出情感图像检索的4个主要内容：图像感性特征的抽取、用户情感信息的计算机描述、情感用户模型的建立和用户模型的个性化；并对这4个主要内容的现有算法和研究进展进行归纳和总结；接着介绍3个典型的原型系统；最后从情感数据库、用户模型的评估和用户模型的计算3个方面阐明实现情感图像检索所面临的问题，并提出一些初步的解决思路。     

一种基于区域的图像检索方法的研究

针对目前基于全局特征的图像检索系统存在的局限性,提出了一种基于区域的检索方案.首先应用K均值聚类算法将图像中的像素按颜色进行聚类,每一类近似对应于图像中的一个一致性区域,在区域上提取颜色和纹理特征.这种方式将检索过程深入到图像内部的物体中去,在一定程度上体现了图像的语义特性;在相似性匹配阶段,提出了一种基于区域的相似性匹配算法,并在实验中证明了其有效性.     

基于内容的图像检索技术的研究与实现

如何为内容丰富多变的大量图像数据编制索引并利用该索引进行高效地相似检索是研究的核心问题。相似图像检索系统通过图像特征提取器提取图像的特征,提供访问图像内容的方法;距离函数是用来计算这些特征之间相似程度的主要工具。实验证明,该系统可以高效地为用户检索出指定特征的图像,对实际应用具有重要的价值。     

Content-based image retrieval using local visual attention feature

Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, salient point based image retrieval has attracted many researchers. However, the related work is usually very time consuming, and some salient points always may not represent the most interesting subset of points for image indexing. Based on fast and performant salient point detector, and the salient point expansion, a novel content-based image retrieval using local visual attention feature is proposed in this paper. Firstly, the salient image points are extracted by using the fast and performant SURF (Speeded-Up Robust Features) detector. Then, the visually significant image points around salient points can be obtained according to the salient point expansion. Finally, the local visual attention feature of visually significant image points, including the weighted color histogram and spatial distribution entropy, are extracted, and the similarity between color images is computed by using the local visual attention feature. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal.     

Expert content-based image retrieval system using robust local patterns

A new image indexing and retrieval algorithm for content based image retrieval is proposed in this paper. The local region of the image is represented by making the use of local difference operator (LDO), separating it into two components i.e. sign and magnitude. The sign LBP operator (S_LBP) is a generalized LBP operator. The magnitude LBP (M_LBP) operator is calculated using the magnitude of LDO. A robust LBP (RLBP) operator is presented employing robust S_LBP and robust M_LBP. Further, the combination of Gabor transform and RLBP operator has also been presented. The robustness is established by conducting four experiments on different image database i.e. Corel 1000 (DB1), Brodatz texture database (DB2) and MIT VisTex database (DB3) under different lighting (illumination) and noise conditions. Investigations reveal a promising achievement of the technique presented when compared to S_LBP and other existing transform domain techniques in terms of their evaluation measures.     

基于多示例学习的图像检索方法

多示例学习对处理各类歧义问题有较好的效果,将它应用于周像检索问题,提出了一种新的基于多示例学习的图像检索方法。首先提取每幅图像的局部区域特征,通过对这些特征聚类求得一组基向量,并利用它们对每个局部特征向量进行编码,接着使用均值漂移聚类算法对图像进行分割,根据局部特征点位置所对应的分割块划分特征编码到相应的子集,最后将每组编码子集聚合成一个向量,这样每幅图像对应一个多示例包。根据用户选择的图像生成正包和反包,采用多示例学习算法进行学习,取得了较为满意的结果。     

Integration of wavelet transform,Local Binary Patterns and moments for content-based image retrieval

The proliferation of large number of images has made it necessary to develop systems for indexing and organizing images for easy access. This has made Content-Based Image Retrieval (CBIR) an important area of research in Computer Vision. This paper proposes a combination of features in multiresolution analysis framework for image retrieval. In this work, the concept of multiresolution analysis has been exploited through the use of wavelet transform. This paper combines Local Binary Pattern (LBP) with Legendre Moments at multiple resolutions of wavelet decomposition of image. First, LBP codes of Discrete Wavelet Transform (DWT) coefficients of images are computed to extract texture feature from image. The Legendre Moments of these LBP codes are then computed to extract shape feature from texture feature for constructing feature vectors. These feature vectors are used to search and retrieve visually similar images from large database. The proposed method has been tested on five benchmark datasets, namely, Corel-1K, Olivia-2688, Corel-5K, Corel-10K, and GHIM-10K, and performance of the proposed method has been measured in terms of precision and recall. The experimental results demonstrate that the proposed method outperforms some of the other state-of-the-art methods in terms of precision and recall.     

基于颜色-纹理自相关算法的内镜图像检索

为了同时有效的提取内镜图像的颜色和纹理信息,体现各特征的空间相关性,本文提出了一种颜色-纹理自相关算法应用于内镜图像检索.即通过纹理谱和自动阈值分割算法提取出纹理图像,并与颜色特征向量相融合再计算其相关性,得到距离分别为1、3的颜色-纹理自相关向量.在相似度匹配阶段采用Generalized Tversky Index...     

图像区间模糊模型及其在图像检索中的应用

在HSV颜色空间下,通过非均匀量化方法,构建了图像区间模糊模型.在此模型下,图像可以看作是一个区间模糊集合(IVFS,interval-valued fuzzy set).IVFS之间相似性度量可以用来衡量图像之间的相似程度.给出一种基于LP范数距离的IVFS集合度量(简称IVFSLp)并将它应用于图像检索中.实验数据表明:IVF-SLP与直方图距离(histogram distance)和普通模糊集的相似性度量(general fuzzy similarity measure)相比具有更好的性能.