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.     

快速小波熵在图像检索中的应用

图像检索技术是多媒体应用中的关键技术,现有的基于内容的图像检索技术大都是基于非压缩域的.随着各种压缩标准的推出与普及,人们开始研究直接在压缩域的图像检索技术.由于小波变换在图像压缩中的应用,文献中提出了基于小波直方图的图像检索技术,但该技术的计算复杂性高,检索速度较慢.提出了快速小波熵的图像检索技术以减少小波直方图技术的计算复杂性并提高了检索速度.     

基于内容的图像检索技术研究

随着互联网技术的快速发展,传统的基于关键字的图像检索已无法满足人们的需要,基于内容的图像检索技术(CBIR)越来越受到人们的青睐.现阐述了基于内容的图像检索系统的组成和基本原理,并着重介绍了CBIR的特征提取,相关反馈的关键技术,最后指出了基于内容的图像检索存在的问题和发展方向.     

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

基于内容的图像检索技术在数字图书馆、网络信息安全、预防犯罪、知识产权、医疗诊断、地理信息系统及遥感等领域有着广泛的应用。他是目前图像检索技术中比较前沿的研究热点。对基于内容的检索技术的研究意义和研究现状进行了阐述;着重介绍了基于内容(颜色、纹理、形状)的图像检索技术;最后介绍了基于内容的图像检索的发展方向。     

基于内容检索的图像数据库流水索引法的研究

图像数据库容量的增长,需要研究高效的索引技术来支持快速相似性检索的要求。总结了图像数据库检索技术的发展轨迹和特点,针对基于内容的图像检索技术中的局限性,从计算机底层硬件的角度提出了基于内容检索的流水索引法。该方法将基于内容的图像检索技术与CpU流水线结构紧密结合,对检索算法进行优化,通过举例比较,说明可提高图像数据库基于内容检索的速度。     

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.     

基于潜在语义分析和相关反馈的图像检索方法

为了解决传统的CBIR系统中存在的"语义鸿沟"问题,提出一种基于潜在语义索引技术(LSI)和相关反馈技术的图像检索方法.在进行图像检索时,先在HSV空间下提取颜色直方图作为底层视觉特征进行图像检索,然后引入潜在语义索引技术试图将底层特征赋予更高层次的语义含义;并且结合相关反馈技术,通过与用户交互进一步提高检索精度.实验...     

一种基于区域的动态分块图象检索方法

本文提出一种新颖的基于内容和图像检索方法，基于运动子块分割并根据视觉特性对不同区域做不同的加权，比较各子块相似度，分析相似度矩阵，并检索查询物体。通过将图象分割细化，充分利用了原图的颜色位置信息，通过实验，实现了对特定物体进行检索。该物体检索方法可进一步发展，为特定的后续处理奠定基础，如在人脸识别等功能中发挥重要作用。     

CLUE: cluster-based retrieval of images by unsupervised learning.

In a typical content-based image retrieval (CBIR) system, target images (images in the database) are sorted by feature similarities with respect to the query. Similarities among target images are usually ignored. This paper introduces a new technique, cluster-based retrieval of images by unsupervised learning (CLUE), for improving user interaction with image retrieval systems by fully exploiting the similarity information. CLUE retrieves image clusters by applying a graph-theoretic clustering algorithm to a collection of images in the vicinity of the query. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. CLUE can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus, it may be embedded in many current CBIR systems, including relevance feedback systems. The performance of an experimental image retrieval system using CLUE is evaluated on a database of around 60,000 images from COREL. Empirical results demonstrate improved performance compared with a CBIR system using the same image similarity measure. In addition, results on images returned by Google's Image Search reveal the potential of applying CLUE to real-world image data and integrating CLUE as a part of the interface for keyword-based image retrieval systems.     

Efficient Cloud Image Retrieval System Using Weighted-Inverted Index and Database Filtering Algorithms

With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scale image/video databases is the most important retrieval control target. In this paper, a cloud based content-based image retrieval (CBIR) scheme is presented. Database-categorizing based on weighted-inverted index (DCWⅡ) and database filtering algorithm (DFA) is used to speed up the features matching process. In the DCWⅡ, the weights are assigned to discrete cosine transform (DCT) coefficients histograms and the database is categorized by weighted features. In addition, the DFA filters out the irrelevant image in the database to reduce unnecessary computation loading for features matching. Experiments show that the proposed CBIR scheme outperforms previous work in the precision-recall performance and maintains mean average precision (mAP) about 0.678 in the large-scale database comprising one million images. Our scheme also can reduce about 50% to 85% retrieval time by pre-filtering the database, which helps to improve the efficiency of retrieval systems.     

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.     

一种改进的颜色匹配对图像检索算法

以子块直方图彩色图像检索算法为基础， 分析了进一步利用图像空间相似信息的颜色匹配对检索算法的性能。在子块直方图的构成、直方图距离值的归类等方面提出了行之有效的改进方法；给出了子块大小、相似度阈值等参数选择的优化原则，使查准率、查全率等检索性能指标得到了较大的提高，得出了几个有用的结论并形成了实验系统。     

基于内容图像分类技术中的特征分析

论文介绍了基于内容的图像检索技术(CBIR)的研究现状和相关技术,其中,特征提取是整个图像分类的关键,色彩和纹理都是CBIR常用到的图像视觉特征。文中提取了图像的颜色和纹理等六种特征．将所有的特征向量进行相应的组合,并采用SVM进行分类。最后,作者通过分析不同特征组合的识别效果,揭示了各种特征之间的内在联系,进而得到图像分类中的最佳特征组合。     

A CBIR system based on saliency driven local image features and multi orientation texture features

In Content-based Image Retrieval (CBIR), the user provides the query image in which only a selective portion of the image carries the foremost vital information known as the object region of the image. However, the human visual system also focuses on a particular salient region of an image to instinctively understand its semantic meaning. Therefore, the human visual attention technique can be well imposed in the CBIR scheme. Inspired by these facts, we initially utilized the signature saliency map-based approach to decompose the image into its respective main object region (ObR) and non-object region (NObR). ObR possesses most of the vital image information, so block-level normalized singular value decomposition (SVD) has been used to extract salient features of the ObR. In most natural images, NObR plays a significant role in understanding the actual semantic meaning of the image. Accordingly, multi-directional texture features have been extracted from NObR using Gabor filter on different wavelengths. Since the importance of ObR and NObR features are not equal, a new homogeneity-based similarity matching approach has been devised to enhance retrieval accuracy. Finally, we have demonstrated retrieval performances using both the combined and distinct ObR and NObR features on seven standard coral, texture, object, and heterogeneous datasets. The experimental outcomes show that the proposed CBIR system has a promising retrieval efficiency and outperforms various existing systems substantially.