Image retrieval based on index compressed vector quantization

Increased amount of visual data in several applications necessitates content-based image retrieval. Since most of visual data is stored in compressed form, it is crucial to develop indexing techniques for searching images based on their content in compressed form. Therefore, it is desirable to explore image compression techniques with capability of describing image content in compressed form. Vector Quantization (VQ) is a compression scheme that exploits intra-block correlation and image correlation reflects image content, hence VQ is a suitable compression technique for compressed domain image retrieval.This paper introduces a novel indexing scheme for compressed domain image databases based on indices generated from IC-VQ. The proposed scheme extracts image features based on relationship between indices of IC-VQ compressed images. This relationship detects contiguous regions of compressed image based on inter- and intra-block correlation. Experimental results show effectiveness superiority of the new scheme compared to VQ and color-based schemes.     

Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales. A cultural heritage image is one of the fine-grained images because each image has the same similarity in most cases. Using the classification technique, distinguishing cultural heritage architecture may be difficult. This study proposes a cultural heritage content retrieval method using adaptive deep learning for fine-grained image retrieval. The key contribution of this research was the creation of a retrieval model that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cultural heritage image. The goal of the proposed method is to perform a retrieval task for classes. Incremental learning for new classes was conducted to reduce the re-training process. In this step, the original class is not necessary for re-training which we call an adaptive deep learning technique. Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learning and image processing. We analyze the experimental results of incremental learning for fine-grained images with images of Thai archaeological site architecture from world heritage provinces in Thailand, which have a similar architecture. Using a fine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category. The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent. Adaptive deep learning for fine-grained image retrieval was used to retrieve cultural heritage content, and it outperformed state-of-the-art methods in fine-grained image retrieval.     

基于树状小波分解的纹理图象检索

针对图象检索应具有简单、快速、有效等要求,提出了一种采用树状小波分解特征的纹理图象检索方法,该方法可以在相应的能量准则下,自适应地对图象进行了带分解,同时可利用小波函数分解的多分辨率与多方向特性,来形成能够在一定程度上对图象进行精确描述的特征矢量;在此基础上,又采用基于图象特征值的主分量分析方法,有效降低了特征矢量的维数;另外,基于用户需求的分层检索,还满足了用户不同层次的需求。实验结果表明,该算法快速,有效,具有较强的应用价值。     

基于内容的SIFT+LSH管道缺陷检索算法研究

各个城市对地下管道安全的检测一直是研究的热点和难点。传统的检测仪器不仅费时费力而且误检率特别高,随着技术的发展计算机视觉相关的方法也有在管道检测中应用,但是检测的速度和效果不尽人意。针对当前传统的检测方法操作复杂,成本高的问题,提出了一种基于内容的SIFT+LSH管道缺陷图像检索方法。该方法首先选取了优势较为明显的局部特征SIFT,充分利用了管道缺陷图像的特征,同时选取LSH算法对图像SIFT特征进行优化,将其转化为Hash编码,提高了检索速度。实验结果表明,基于SIFT特征和LSH算法的管道缺陷检索方法,相比与传统的SIFT特征和欧式距离的检索方法,大大提高了检索的速度,使得检测人员在实际操作中能够更快地获取到比较满意的检索结果。     

A survey of content-based image retrieval with high-level semantics

In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the ‘semantic gap’: (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users’ intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested.     

基于Sobel算子的快速图像匹配检索方法

给出一种较目前多数检索方法更为高效,快速的图像匹配检索方法。本算法首先利用Sobel算子对原始图像的边缘信息进行处理,然后对图像进行二值化处理,对所得的图像矩基于边缘进行分块实体提取,再将实体与目标图像矩进行比对处理,通过阀值的设置来判断图像的匹配程度,进而达到图像检索的目的。     

基于形状方向能量的图像检索①

基于形状特征的检索技术是图像检索的研究热点之一,提出一种基于图像形状方向能量的检索方法一一先对图像进行一些基本的处理：去噪、边缘获取、细化边缘、调整角度,然后根据图像8个方向的能量累积,归一化处理,得到八个方向的能量累积数据。最后简述了图像智能检索系统的设计,应用该智能检索系统,给出了一些图片的基于方向能量检索方法的检索效果。     

基于内容的图象检索系统

分析了传统的图象检索方法的困难,比较了基于内容的图象检索系统所具有的优点、系统结构及一般的处理方法。着重探讨了视觉特征的提取及表示、图象检索的性能优化方法及现有图象检索系统。最后,对基于内容的图象检索提出了一些值得进一步研究的问题。     

基于整体区域相似的互动式图象检索模型

高效的基于内容的图象检索在许多领域得到了广泛的应用 ,基于内容的图象检索研究领域已经建立了一些系统 ,但在实际使用中 ,这些系统均有如下欠缺 :(1)这些系统均期望以相同的方法来处理各种不同类型的图象检索 ;(2 )这些系统在设计时 ,均缺乏从使用者的需求出发 .实际上 ,由于不同的检索方式是针对不同类型的图象 ,为此 ,提出了一个基于整体区域相似匹配的图象互动式检索系统 ,该系统是一个基于小波变换的特征提取和图象整体区域相似的、语义分类和互动方法的图象检索系统 .与其他检索方法相比较 ,此方法允许自适应查找和互动 ,因此可缩小查找范围 ,以提高检索效率 .实验结果表明 ,该系统比其他一些系统精确和高效     

一种基于知识的图象信息表示与检索系统

给出了一种使用一阶谓词逻辑（Prolog语言）为图象内容建模,并结合知识库,实现高效图象检索的方案。基于知识的图象信息表示与检索系统（KIRS）将图象的人工注释、机器自动提取的图象低层信息以及知识库中的知识统一于一致的概念,以知识推理的方式进行检索。     

语义图像检索研究进展

语义图像检索已成为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”问题的关键。从图像语义描述方式、图像语义抽取方法和语义检索系统设计3个方面对语义图像检索的研究状况进行了分析和研究;讨论了面向对象的图像内容模型和图像语义表示问题;对利用系统知识的提取、根据用户交互的提取和利用外部信息源的语义生成等具有代表性的语义处理方法进行了阐述;介绍了系统设计中用户界面和语义处理的不同方式,最后从对象识别、语义抽取规则、用户检索模型和图像检索性能评价标准4个方面剖析了实现图像语义处理所面临的困难,并提出了一些初步解决思路。     

利用Gabor滤波器的基于内容的图像检索

分析了利用一组Gabor滤波器进行基于内容图象检索的方法。该方法应用于西北大学可视化研究所开发的系统中,经试验分析与性能评价,该方法在医学图像的检索中取得了较好的检索效果。该文的试验和分析对选择和优化检索算法有一定的参考价值。     

全局及其个性化区域特征的图像检索

交通领域个性化图像检索的关键是根据业务需求通过重点监控车辆的个性化特征 在海量数据库中进行匹配,其目的是捕获与重点监控车辆相关的卡口信息。目前的图像检索算法包括基于文本的图像检索、基于内容的图像检索方法和基于语义的图像检索。针对交通领域的图像检索需求,提出了一种基于全局以及个性化感兴趣区域特征的图像检索算法。通过使用交通图像库进行检索验证,对个性化特征进行精准滤除,从而得到准确的检索结果。实验表明,此种基于全局特征结合个性化感兴趣区域特征的图像检索算法解决了CNN高层特征对个性化局部特征描述能力低、检索耗时等问题,并通过个性化局部特征提高了检索效果,使得检索率、平均准确率都达到90%,呈现出较好的检索效果,计算速度快,具有较强的鲁棒性和实用性。     

模糊图像检索技术及应用研究

提出了一种基于模糊空间的图像数据模型,给出了模糊相似性度量方法,结合水利工程图像查询的具体应用讨论了在模糊空间中图像的检索过程。     

基于GrabCut改进算法的服装图像检索方法

为了消除服装图像背景的影响,针对目前的GrabCut算法存在对图像局部像素值的变化敏感、时间开销大、边缘不准确等问题,提出了改进的GrabCut算法。在改进算法中,通过对梯度图像使用多尺度分水岭去噪增强了图像的边缘信息,减少了后续处理的计算量;通过采取熵惩罚因子最优能量函数减少了检索图像的有效信息丢失。将改进后的GrabCut算法引入基于内容的服装图像检索系统中,实验结果表明与同类方法相比,所提方法在检索显示准确性以及检索的平均查准率和查全率方面均有明显的提升。     

数字图书馆中基于内容的图像检索技术

随着多媒体技术的飞速发展,多媒体信息的日益丰富,多媒体信息检索技术已成为当前研究的热点.本文着重介绍了基于内容的图像检索技术及其发展,分析了图像信息的多种特征提取方法,最后,给出了一个基于综合特征提取的图像检索系统实例.     

基于图论的无监督区域遥感图像检索算法研究

为了改善基于内容的遥感图像检索技术,以遥感图像区域检索为框架,提出了一种新的基于图论的无监督学习遥感图像检索算法。首先,提出的方法 用图表为每一幅图像建模,同时结合局部信息和相关的空间结构,提供基于区域的图像代表。将每一幅图像初步划分为不同的区域,再通过属性关系图建模,节点和边界分别代表区域特征和它们之间存在的空间关系。然后,通过评估基于图像的相似点实现最相似于查询图像的图像检索。为匹配相应的图像以及按照图像相似点实现图像检索,采用了结合子图同构算法和光谱图嵌入技术的新型非精确图像匹配策略。实验结果显示,与其他两种无监督遥感图像检索方法相比,所提方法的检索性能明显改善。     

基于感兴趣区域的彩色图像检索

随着图像信息的大规模应用,图像检索具有越来越重要的应用价值.基于内容的图像检索成为今年来的研究重点.提出一种基于感兴趣区域(ROT)和颜色一纹理特征的联合彩色图像检索新方法.采用此方法,将图像经由对图像进行小波变换,得到小波变换域的颜色局部能量,进而得到图像分割后各区域颜色和纹理综合特征,并采用用户定义感兴趣区域即ROI(Region-of-Interest)对相似度进行修正.实验结果证明,该方法能够快速有效地得到图像特征和图像中的感兴趣物体,取得很好的检索效果.     

Developing an object‐oriented framework for content‐based image retrieval

Content‐based image retrieval (CBIR) is a process of retrieving images from an image database by exploiting the content of the images (typically the querying of an image). CBIR avoids many problems associated with traditional ways of retrieving images by keywords. Thus, a growing interest in the area of CBIR has been established in recent years. In this paper, a novel object‐oriented framework (CBIRFrame) is built for CBIR applications development. We discuss the motivations for CBIRFrame before discussing its design in detail. Two applications of CBIRFrame are also briefly discussed to show the effectiveness of applying CBIRFrame to real applications. Finally, we outline the possible uses of the design of CBIRFrame for other types of domains, such as content‐based retrieval of video clips. Copyright © 2003 John Wiley & Sons, Ltd.