共查询到18条相似文献，搜索用时 218 毫秒
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基于内容的图像检索方法 总被引：4，自引：0，他引：4
阐述了基于内容的图像检索(CBIR)的原理，特点。对于该技术与传统的基于文本的图像检索方法作了比较，介绍了几种基于内容的图像检索的方法，回顾了基于内容的图像检索研究以及基于内容的检索方法中有待研究的问题。 相似文献
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随着图像检索技术近几年来的快速发展，基于内容图像检索和基于文本图像检索两种技术的不和谐现象越来越明显；两者各自所对应的元数据集之间很难兼容；基于内容图像检索和图像元数据联系相对薄弱。本文正是针对这样一种不协调的情况，从用户对图像检索的需求出发，以图像元数据标准为平台，对基于内容图像检索和基于文本图像检索的融合问题做一探讨，这有利于解决图像检索中存在的有关兼容问题。 相似文献
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概述了基于文本的图像检索和基于内容的图像检索关键技术,阐述了基于这两种图像检索技术下的不同的检索途径和检索策略,并从用户的角度对二者做了比较研究。 相似文献
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针对传统的基于关键字Web图像检索中的语义缺失问题,结合Web图像的视觉特征和描述文本,利用本体描述Web图像的语义特征,构建了基于本体的Web图像语义检索模型。该模型以领域本体描述Web图像的语义特征,通过自动图像标注技术构建Web图像本体库,用户可以通过输入关键词或者提交示例图像进行图像检索。实验表明,该模型具有较高的图像检索准确率。 相似文献
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信息检索在信息飞速增长的今天具有十分重大的意义。作为信息检索的一个重要的部分,图像检索得到了广泛的研究。由于基于文本的图像检索的种种不利因素,基于内容的图像检索成为目前的主流方向,本文提出一种基于空间聚类的方法,用图像的颜色特征来计算图像之间距离,采用DBSCAN算法对图像聚类,得到检索结果。 相似文献
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基于内容的图像检索研究进展 总被引：5，自引：0，他引：5
基于内容的图像检索是信息检索领域的研究热点之一,论文论述了基于内容图像数据库的体系结构,基于颜色特征、纹理特征、形状特征、对象空间关系的图像检索等内容。 相似文献
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基于内容的图像信息检索综述 总被引：13，自引：0，他引：13
基于内容的图像检索技术，即从大量的静止或活动视频图像库中检索包含目标物体的图像（或视频片段），在高度信息化的今天，已成为内容图像库中图像信息组织和管理不可缺少的技术，本文介绍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、纹理等静止图像检索技术以及视频检索技术进行了讨论。 相似文献
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数字图书馆中基于内容的图像搜索引擎 总被引：1，自引：0，他引：1
介绍了图像搜索引擎框架、现有的基于内容的图像搜索引擎、基于内容的图像搜索引擎中图像分类方法以及基于内容的图像检索技术,并分析了基于内容的图像搜索引擎发展趋势。 相似文献
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《Information processing & management》2005,41(2):177-194
In this paper, we propose a re-ranking algorithm using post-retrieval clustering for content-based image retrieval (CBIR). In conventional CBIR systems, it is often observed that images visually dissimilar to a query image are ranked high in retrieval results. To remedy this problem, we utilize the similarity relationship of the retrieved results via post-retrieval clustering. In the first step of our method, images are retrieved using visual features such as color histogram. Next, the retrieved images are analyzed using hierarchical agglomerative clustering methods (HACM) and the rank of the results is adjusted according to the distance of a cluster from a query. In addition, we analyze the effects of clustering methods, query-cluster similarity functions, and weighting factors in the proposed method. We conducted a number of experiments using several clustering methods and cluster parameters. Experimental results show that the proposed method achieves an improvement of retrieval effectiveness of over 10% on average in the average normalized modified retrieval rank (ANMRR) measure. 相似文献
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M.T. Martín-Valdivia M.C. Díaz-GalianoA. Montejo-Raez L.A. Ureña-López 《Information processing & management》2008
Nowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval techniques (particularly images retrieval) have become an active research direction. In the past few years, a lot of content-based image retrieval (CBIR) systems have been developed. However, despite the progress achieved in the CBIR, the retrieval accuracy of current systems is still limited and often worse than only textual information retrieval systems. In this paper, we propose to combine content-based and text-based approaches to multi-modal retrieval in order to achieve better results and overcome the lacks of these techniques when they are taken separately. For this purpose, we use a medical collection that includes both images and non-structured text. We retrieve images from a CBIR system and textual information through a traditional information retrieval system. Then, we combine the results obtained from both systems in order to improve the final performance. Furthermore, we use the information gain (IG) measure to reduce and improve the textual information included in multi-modal information retrieval systems. We have carried out several experiments that combine this reduction technique with a visual and textual information merger. The results obtained are highly promising and show the profit obtained when textual information is managed to improve conventional multi-modal systems. 相似文献
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Avi Arampatzis Konstantinos Zagoris Savvas A. Chatzichristofis 《Information processing & management》2013
Content-based image retrieval (CBIR) with global features is notoriously noisy, especially for image queries with low percentages of relevant images in a collection. Moreover, CBIR typically ranks the whole collection, which is inefficient for large databases. We experiment with a method for image retrieval from multimedia databases, which improves both the effectiveness and efficiency of traditional CBIR by exploring secondary media. We perform retrieval in a two-stage fashion: first rank by a secondary medium, and then perform CBIR only on the top-K items. Thus, effectiveness is improved by performing CBIR on a ‘better’ subset. Using a relatively ‘cheap’ first stage, efficiency is also improved via the fewer CBIR operations performed. Our main novelty is that K is dynamic, i.e. estimated per query to optimize a predefined effectiveness measure. We show that our dynamic two-stage method can be significantly more effective and robust than similar setups with static thresholds previously proposed. In additional experiments using local feature derivatives in the visual stage instead of global, such as the emerging visual codebook approach, we find that two-stage does not work very well. We attribute the weaker performance of the visual codebook to the enhanced visual diversity produced by the textual stage which diminishes codebook’s advantage over global features. Furthermore, we compare dynamic two-stage retrieval to traditional score-based fusion of results retrieved visually and textually. We find that fusion is also significantly more effective than single-medium baselines. Although, there is no clear winner between two-stage and fusion, the methods exhibit different robustness features; nevertheless, two-stage retrieval provides efficiency benefits over fusion. 相似文献
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针对网络邮票图像的特点,提出邮票领域本体构建方法。根据网络邮票图像的视觉特征和描述文本．利用本体描述其语义特征,通过自动图像标注技术构建邮票图像本体库,并构建网络邮票图像的语义检索系统。实验表明,该系统解决了网络图像基于关键字检索和基于内容检索中的语义缺失问题,具有较高的图像检索准确率。 相似文献
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基于内容的图像检索（CBIR,Content—based Image Retrieval）技术是图像领域研究的热点问题之一。介绍了图像检索系统相关算法的基本原理,采用的是基于改进的颜色直方图的算法,结合欧氏距离算法来进行图像处理和计算。选用Visual C＋＋开发工具结合CxImage类库实现图像检索系统。用户可以选择关键图和图片库,之后系统就对关键图和图像库进行特征提取,将关键图与图片库的每一张图片相应特征进行对比,并计算关键图与图像库中每幅图片的相似度,最后按指定相似度大小输出检索结果显示给用户。 相似文献
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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. 相似文献