一种新的基于SVM相关反馈的图像检索算法

提出了一种结合颜色和形状特征的图像检索方法,针对传统基于内容图像检索不能很好满足用户需求的问题,提出了一种基于支持向量机（SVM）的相关反馈算法来捕捉用户的检索意图。实验结果证明,算法能发挥用户在检索过程中的作用,具有较好检索性能。     

数字图书馆中基于内容的图像检索研究

针对数字图书馆中的图像检索问题,提出了一种基于SVM的相关反馈算法。比较了基于SVM的相关反馈方法和MARS方法的检索性能,在一定程度上解决了基于内容的图像检索中由于底层特征和上层理解之间的差异而造成的＂语义鸿沟＂问题,仿真实验表明这种算法能提高数字图书馆中图像检索的效率和查全率。     

基于信息度量与SVM的文本图像自动提取技术

在网络文本图像的自动提取过程中,计算机难以直接实现基于高层语义特征的文本图像提取,因此文本图像提取技术的性能很走程度上依赖于底层统计特征的提取。广义归一化图像信息度量（GNPIM）和Lorenz信息度量（LIM）在灰度级上描述了图像的分布,在语义层上反映了图像的内容,是区分文本图像和一般连续色调图像的有效统计特征,作为支持向量机（SVM）的输入向量。可区分文本图像与连续色调图像,从而实现网络中文本图像的自动提取。实验结果表明,基于GNPIM、LIM与SVM的文本图像提取技术能够有效提取网络中的文本图像。且正确率高,速度快。     

基于内容的图像检索的相关性反馈机制

介绍基于内容的图像检索技术，阐述了一种新的相关性反馈机制，它通过对用户指定的相关图像及不相关图像的特征分布进行统计分析来动态更新相似性度量和查询，从而更准确地表达用户特定的信息需求，提高了检索系统的性能。     

基于内容的图像检索技术研究现状综述

基于内容特征的图像库检素技术虽然开辟了一条更为准确直观的检索途径,但由于图像内容特征很难准确提取和描述,特征的相似度计算与人眼的感知存在一定的差异,使其会受到知识领域和检索任务的限制.     

基于内容的图像检索技术

为了提高图像资源利用率,快速、有效地查询和检索数据库中的图像,基于内容的图像检索技术（CBIR）便应运而生。本文分析和比较了基于颜色、纹理、形状等图像检索技术的主要方法,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探讨

基于内容的图像检索(Content-based Image Retrieval)技术是当今的一个研究热点.本文介绍了基于内容的图像检索技术的基本原理、特点、体系结构和一般过程,并主要介绍了CBIR中的关键技术,最后指出了今后的研究方向.     

基于SVM的花岗岩薄片图像石英分割方法研究

花岗岩薄片不同矿物成分在图像中可能存在相同灰度分布甚至相似纹理。通过结合正交偏光镜镜下图片干涉色信息和单偏光镜图片信息分析,提高了花岗岩薄片石英分割的准确率。方法是从单偏光镜和正交偏光镜下图片中提取图像块的特征信息,运用支持向量机分类方法对图像块进行分类,在分类为石英的图像块中选取种子,再根据两张图片信息,用区域生长方法完成石英分割。该方法实现了自动化分割,分割准确率高,实验证明该方法切实可行。     

基于灰色系统理论的相关反馈图像检索算法

探讨了灰色系统理论在相关反馈图像检索中的应用,提出了一种基于灰色系统理论的相关反馈图像检索算法。动态地更新查询向量。从而使图像检索结果与人的主观感知更加接近。     

一种基于贴近度的图片内容检索技术研究

基于内容检索的实质是将图像的底层特征进行采集提取,而后对提取的图像特征进行检索,当然提取特征与特征检索手段多种多样。为了刻画两幅图像相似特征集合之间的相似与接近程度问题,对若干相似性度量标准之间的关系进行了讨论,提出解决方案并最终实现。     

XMage: An image retrieval method based on partial similarity

XMage is introduced in this paper as a method for partial similarity searching in image databases. Region-based image retrieval is a method of retrieving partially similar images. It has been proposed as a way to accurately process queries in an image database. In region-based image retrieval, region matching is indispensable for computing the partial similarity between two images because the query processing is based upon regions instead of the entire image. A naive method of region matching is a sequential comparison between regions, which causes severe overhead and deteriorates the performance of query processing. In this paper, a new image contents representation, called Condensed eXtended Histogram (CXHistogram), is presented in conjunction with a well-defined distance function CXSim() on the CX-Histogram. The CXSim() is a new image-to-image similarity measure to compute the partial similarity between two images. It achieves the effect of comparing regions of two images by simply comparing the two images. The CXSim() reduces query space by pruning irrelevant images, and it is used as a filtering function before sequential scanning. Extensive experiments were performed on real image data to evaluate XMage. It provides a significant pruning of irrelevant images with no false dismissals. As a consequence, it achieves up to 5.9-fold speed-up in search over the R*-tree search followed by sequential scanning.     

Probabilistic co-relevance for query-sensitive similarity measurement in information retrieval

Interdocument similarities are the fundamental information source required in cluster-based retrieval, which is an advanced retrieval approach that significantly improves performance during information retrieval (IR). An effective similarity metric is query-sensitive similarity, which was introduced by Tombros and Rijsbergen as method to more directly satisfy the cluster hypothesis that forms the basis of cluster-based retrieval. Although this method is reported to be effective, existing applications of query-specific similarity are still limited to vector space models wherein there is no connection to probabilistic approaches. We suggest a probabilistic framework that defines query-sensitive similarity based on probabilistic co-relevance, where the similarity between two documents is proportional to the probability that they are both co-relevant to a specific given query. We further simplify the proposed co-relevance-based similarity by decomposing it into two separate relevance models. We then formulate all the requisite components for the proposed similarity metric in terms of scoring functions used by language modeling methods. Experimental results obtained using standard TREC test collections consistently showed that the proposed query-sensitive similarity measure performs better than term-based similarity and existing query-sensitive similarity in the context of Voorhees’ nearest neighbor test (NNT).     

Computing,explaining and visualizing shape similarity in content-based image retrieval

Although there is a growing need for Content-Based Image Retrieval systems, their use is often hampered by significant computational complexity and their inability to explain to their users the reasoning behind the similarity and retrieval processes they employ. This paper introduces Turning Function Difference (TFD), an efficient novel shape-matching method, which is based on the curvature of the shape outline and is translation, rotation and scale invariant. The method produces information about the correspondence of points belonging to the compared shapes that are used during the explanation process. TFD explains its results through an alignment and a visual animation process that highlights the similarities between the model images and each one of the selected images as perceived by the method. The proposed shape-matching method is used in the G Computer Vision (GCV) library, a single-object image retrieval system that utilizes information about the objects’ outlines and explains the reasoning behind the selection of similar images to the user. The implemented system is freely available for download to all interested users.     

西藏湖泊TM影像遥感分析

本文利用TM影像及数据，对西藏湖泊做了全面的分析探讨，总结了湖泊的遥感影像特征，建立了遥感解译标志，并利用不同盐度水体的反射率和主成份分析等对湖泊做了遥感信息的探索性研究。以此为依据，对西藏全区湖泊进行了判读，识别出大于1km^2的湖泊共791个，为以后从事西藏湖泊及盐湖研究奠定了良好的基础。     

一种基于曲波域的SAR图像特征增强新方法

Starck等人的图像增强方法不能有效增强SAR图像中的边缘特征.为此,提出一种curvelet域SAR图像特征增强新方法.该方法充分利用curvelet变换多尺度多方向特性及其良好的各向异性特点,在curvelet域内提取图像的边缘特征,并定位特征curvelet系数.通过增强特征curvelet系数,达到增强图像边缘特征的目的.实验结果表明,与Starck等人的方法相比,本文算法能够更加有效性地增强SAR图像的边缘特征.     

Explore instance similarity: An instance correlation based hashing method for multi-label cross-model retrieval

With the rapid growth of multimedia data, cross-media hashing has gained more and more attention. However, most existing cross-modal hashing methods ignore the multi-label correlation and only apply binary similarity to measure the correlation between two instances. Most existing methods perform poorly in capturing the relevance between retrieval results and queries since binary similarity measurement has limited abilities to discriminate minor differences among different instances. In order to overcome the mentioned shortcoming, we introduce a novel notion of instance similarity method, which is used to evaluate the semantic correlation between two specific instances in training data. Base on the instance similarity, we also propose a novel deep instance hashing network, which utilizes instance similarity and binary similarity simultaneously for multi-label cross-model retrieval. The experiment results on two real datasets show the superiority of our proposed method, compared with a series of state-of-the-art cross-modal hashing methods in terms of several metric evaluations.     

A semantics and image retrieval system for hierarchical image databases

This work presents a content based semantics and image retrieval system for semantically categorized hierarchical image databases. Each module is designed with an aim to develop a system that works closer to human perception. Images are mapped to a multidimensional feature space, where images belonging a semantic are clustered and indexed to acquire its efficient representation. This helps in handling the existing variability or heterogeneity within this semantic. Adaptive combinations of the obtained depictions are utilized by the branch selection and pruning algorithms to identify some closer semantics and select only a part of the large hierarchical search space for actual search. So obtained search space is finally used to retrieve desired semantics and similar images corresponding to them. The system is evaluated in terms of accuracy of the retrieved semantics and precision-recall curves. Experiments show promising semantics and image retrieval results on hierarchical image databases. The results reported with non-hierarchical but categorized image databases further prove the efficacy of the proposed system.     

科技用户数据库检索特征分析

分析科技用户检索的基本特征模型,分别从数据库选择、检索策略选择、检索式选择几个决策阶段分析选择决策特征,再分别从对需求理解的学习、对检索策略的学习、对检索结果评价的学习三个角度对学习问题进行总结。     

Color image retrieval technique based on color features and image bitmap

The field of color image retrieval has been an important research area for several decades. For the purpose of effectively retrieving more similar images from the digital image databases, this paper uses the color distributions, the mean value and the standard deviation, to represent the global characteristics of the image. Moreover, the image bitmap is used to represent the local characteristics of the image for increasing the accuracy of the retrieval system. As the experimental results indicated, the proposed technique indeed outperforms other schemes in terms of retrieval accuracy and category retrieval ability. Furthermore, the total memory space for saving the image features of the proposed method is less than Chan and Liu’s method.