基于内容的图像检索技术

基于内容的图像数据库检索技术是当今的一个研究热点.本文介绍了基于内容图像检索的基本原理、检索方式和关键技术,并列举了几种较为先进的图像检索系统.最后探讨了当前研究中存在的问题以及今后的研究方向.     

基于内容的图像检索在病虫害管理中的应用

综述了我国目前森林病虫害管理的现状以及存在的不足，提出了将基于内容的图像检索技术应用在森林病虫害管理的新思路。然后分析了基于内客的图像检索技术的特点，森林病虫害管理的体系结构、主要技术的研究情况，以及基于内容的图像检索技术在森林病虫害管理中应用的重大意义。     

基于内容的图像检索技术研究

在对海量的图像数据进行检索时,传统的基于数值/字符的信息检索技术并不能满足要求.因此,基于内容的图像检索技术(CBIR:Content-Based Image Retrieval)的研究应运而生,并引起了广泛关注.本文主要讨论CBIR研究中的一些关键问题:图像的内容特征及其提取、特征之间的相似度计算、查询条件的表达、检索性能的评价、压缩域的图像检索技术等等,并指出了一些可值得深入研究的方向.     

基于ROI的图像检索算法研究

针对全局特征的图像检索不能很好地满足用户的意图和基于图像分割的检索过分依赖复杂的图像分割算法二者的不足。在基于子图的检索思想的基础上，给出了一种基于用户感兴趣区域的图像检索算法。该算法无需对图像进行复杂的分割就能提取对象特征，实验证明该算法具有简单、高效、查全率较高的优点。     

基于内容的图像检索技术的专利状况分析

随着多媒体技术的迅速发展,基于内容的图像检索技术已成为近些年来图像检索领域的研究热点.从申请量、申请人国别、申请人排序、技术主题、热点技术对中国专利申请进行了详细的分析,并给出了基于内容的图像检索技术的专利发展趋势,为企业、科研单位的技术创新提供参考.     

基于内容的检索

信息时代的到来使人们接触到越来越多的多媒体信息。有效地组织、管理和检索大规模的多媒体数据库成为当前迫切需要解决的问题。本文介绍了基于内容检索的基本概念及其基本结构。对多媒体数据库的结构给予了简要的介绍,最后描述了图像和视频的基本检索方法。     

基于内容的图像检索方法

综述了基于内容检索技术的进展,并对其主要方法如基于颜色、形状、纹理等图像检索技术进行了论述,介绍了几个典型的基于内容的图像检索系统.通过综述指出了今后的研究方向.     

基于内容的视频检索技术

从分析基于内容的视频检索的优点和系统结构出发，在介绍基于内容的视频检索的一般步骤的基础上，对基于内容的视频检索中的关键技术和多种算法进行了描述和分析，并介绍了基于内容的视频检索的一些最新方法。最后，对基于内容的视频检索提出一些值得进一步研究的问题。     

基于内容的图像检索技术的专利技术综述

随着现代通信技术与多媒体技术的发展,基于内容的图像检索成为了近些年来图像检索领域研究热点.本文以国内外若干著名的基于内容的图像检索系统为主线,结合相关的专利申请对基于内容的图像检索技术的发展历程进行了回顾,分析了当前基于内容的图像检索的热点技术,最后总结了国内外相关专利申请状况,希望对该领域的学术研究和产业发展有所帮助.     

基于内容的视频检索技术研究

研究了视频数据库的基于内容检索技术,对其中的关键技术和各种算法进行了描述及评价,并对其存在的问题和研究方向进行了讨论。     

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.     

Feature selection for content-based image retrieval

In this article, we propose a novel system for feature selection, which is one of the key problems in content-based image indexing and retrieval as well as various other research fields such as pattern classification and genomic data analysis. The proposed system aims at enhancing semantic image retrieval results, decreasing retrieval process complexity, and improving the overall system usability for end-users of multimedia search engines. Three feature selection criteria and a decision method construct the feature selection system. Two novel feature selection criteria based on inner-cluster and intercluster relations are proposed in the article. A majority voting-based method is adapted for efficient selection of features and feature combinations. The performance of the proposed criteria is assessed over a large image database and a number of features, and is compared against competing techniques from the literature. Experiments show that the proposed feature selection system improves semantic performance results in image retrieval systems. This work was supported by the Academy of Finland, Project No. 213,462 (Finnish Centre of Excellence Program 2006–2011).     

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。     

Semantic content-based image retrieval: A comprehensive study

The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts.     

Hybrid textual-visual relevance learning for content-based image retrieval

Learning effective relevance measures plays a crucial role in improving the performance of content-based image retrieval (CBIR) systems. Despite extensive research efforts for decades, how to discover and incorporate semantic information of images still poses a formidable challenge to real-world CBIR systems. In this paper, we propose a novel hybrid textual-visual relevance learning method, which mines textual relevance from image tags and combines textual relevance and visual relevance for CBIR. To alleviate the sparsity and unreliability of tags, we first perform tag completion to fill the missing tags as well as correct noisy tags of images. Then, we capture users’ semantic cognition to images by representing each image as a probability distribution over the permutations of tags. Finally, instead of early fusion, a ranking aggregation strategy is adopted to sew up textual relevance and visual relevance seamlessly. Extensive experiments on two benchmark datasets well verified the promise of our approach.     

System profiles in content-based image indexing and retrieval

In this paper, a novel study on system profiles and adaptation of parameters for end-users of content-based indexing and retrieval (CBIR) applications are presented. The main objective of the study is improving the overall CBIR application performance in different hardware platforms having different technical capabilities and conditions. We define CBIR system profiles in terms of hardware and system platform attributes and propose CBIR parameters for each profile. Hence, the study consists of two main parts: system profiling and adaptation of indexing and retrieval parameters for each profile. The proposed CBIR parameters are appropriate configurations for optimal CBIR use on every platform. The proposed parameters for each system profile are assessed over a large set of experiments. Experimental studies show that the proposed parameters for each system profile have satisfactory semantic retrieval performance, with reduced computational complexity and storage space requirement. 45 to 78% improvement is achieved in the computational complexity of the retrieval process depending on the profile.     

Frequency layered color indexing for content-based image retrieval

Image patches of different spatial frequencies are likely to have different perceptual significance as well as reflect different physical properties. Incorporating such concept is helpful to the development of more effective image retrieval techniques. We introduce a method which separates an image into layers, each of which retains only pixels in areas with similar spatial frequency characteristics and uses simple low-level features to index the layers individually. The scheme associates indexing features with perceptual and physical significance thus implicitly incorporating high level knowledge into low level features. We present a computationally efficient implementation of the method, which enhances the power and at the same time retains the simplicity and elegance of basic color indexing. Experimental results are presented to demonstrate the effectiveness of the method.     

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.     

Similarity-based online feature selection in content-based image retrieval.

Content-based image retrieval (CBIR) has been more and more important in the last decade, and the gap between high-level semantic concepts and low-level visual features hinders further performance improvement. The problem of online feature selection is critical to really bridge this gap. In this paper, we investigate online feature selection in the relevance feedback learning process to improve the retrieval performance of the region-based image retrieval system. Our contributions are mainly in three areas. 1) A novel feature selection criterion is proposed, which is based on the psychological similarity between the positive and negative training sets. 2) An effective online feature selection algorithm is implemented in a boosting manner to select the most representative features for the current query concept and combine classifiers constructed over the selected features to retrieve images. 3) To apply the proposed feature selection method in region-based image retrieval systems, we propose a novel region-based representation to describe images in a uniform feature space with real-valued fuzzy features. Our system is suitable for online relevance feedback learning in CBIR by meeting the three requirements: learning with small size training set, the intrinsic asymmetry property of training samples, and the fast response requirement. Extensive experiments, including comparisons with many state-of-the-arts, show the effectiveness of our algorithm in improving the retrieval performance and saving the processing time.