共查询到17条相似文献，搜索用时 234 毫秒
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从海量图像数据中快速、准确、有效地获取图像信息是图像检索的主要任务。基于文本的图像检索、基于内容的图像检索和基于水印的图像检索是三种最常用的数字图像检索方法。本文详细阐述了不同检索方法的发展及优缺点,并着重分析了基于水印图像检索方法的特点、模型、应用和亟待解决的问题,最后指出了图像检索技术的未来发展和研究方向。 相似文献
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分析CBIR系统的用户查询模式以及基于形状特征的检索系统构建相关技术,在系统构建中应用示例图像与采样图像两种方式对图像的形状特征进行检索,通过图像分割获取目标轮廓,利用轮廓点与兴趣点之间的空间分布关系构造形状描述函数,应用傅立叶变换提取图像特征,最后在系统检索实验中证明其有效性。 相似文献
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大量图像的出现使得图像检索的需求越来越强烈.图像检索系统经历了基于文字的检索和基于底层特征的检索两个阶段.新的检索系统通过机器学习等技术,综合利用了视觉特征和高层语义概念进行更精确的语义检索.本文从图像底层特征表示与抽取、高层语义特征抽取、相关反馈等方面介绍了当前图像检索系统的基本进展,并对图像检索的发展趋势进行了展望. 相似文献
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多媒体搜索引擎创新比较研究 总被引：1，自引：0，他引：1
文章介绍基于文本描述和基于内容的多媒体搜索的工作原理和特点,实证分析不同多媒体搜索引擎的查准率、查全率并分析其特点和原因,创新地提出自动构建多媒体资源的目录层级检索的方法,将目录检索与关键词检索结合起来提高检索效率。 相似文献
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对信息化背景下基于内容的多媒体检索的特征做了简单的介绍,在此基础上分析了这些技术在数字档案馆录像视频、图像信息在内的多媒体档案检索系统的实现过程,指出基于内容的多媒体检索技术势必会和基于文本的传统检索方法有机的结合在一起,从而更好的服务于数字档案馆多媒体档案的利用与管理,为用户提供更加便捷的服务. 相似文献
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商标图像检索技术述评 总被引：2，自引：0，他引：2
对商标图像检索的三种技术(类目检索、文本检索、基于内容检索) 加以介绍, 并重点阐述采用基于内容技术实现对纯图像、图形商标检索的原理和主要方法, 最后分析归纳了实践应用中检索商标图像的障碍及有待解决的问题。 相似文献
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Folksonamy是在web2．0的时代出现的一种新的资源组织方式，为基于文本的图像检索技术（TBIR）提供了新的发展方向。 相似文献
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基于WWW的图像检索技术 总被引：12，自引：1，他引：11
王欣 《现代图书情报技术》2002,18(3):70-73
在阐释WWW图像信息检索基本原理的基础上，着重介绍并评价了目前图像检索领域主要的检索技术，并列举了几种较为先进的图像检索demo系统，最后对一些制约基于WWW图像检索技术发展的瓶颈问题进行了简要的探讨。 相似文献
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基于内容的图像检索技术是对图像的物理内容为加工对象的检索技术之一,主要实现方式包括基于颜色、纹理、形状、空间位置和语义等。其中基于颜色的图像检索发展最为成熟,而基于语义的检索则尚处于探讨、研究阶段。基于内容检索和基于文本检索在数字图书馆中可以融合共同提供检索服务。Google为这一尝试提供了在后控阶段的有效案例,而真正的实现两者的融合是在预处理阶段。两者结合在数字图书馆中的应用是可行的,相信能够提供更好的图像检索服务。 相似文献
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The effectiveness of a video retrieval system largely depends on the choice of underlying text and image retrieval components. The unique properties of video collections (e.g., multiple sources, noisy features and temporal relations) suggest we examine the performance of these retrieval methods in such a multimodal environment, and identify the relative importance of the underlying retrieval components. In this paper, we review a variety of text/image retrieval approaches as well as their individual components in the context of broadcast news video. Numerous components of text/image retrieval have been discussed in detail, including retrieval models, text sources, temporal expansion methods, query expansion methods, image features, and similarity measures. For each component, we conduct a series of retrieval experiments on TRECVID video collections to identify their advantages and disadvantages. To provide a more complete coverage of video retrieval, we briefly discuss an emerging approach called concept-based video retrieval, and review strategies for combining multiple retrieval outputs. 相似文献
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Multimodal biomedical image indexing and retrieval using descriptive text and global feature mapping
Matthew S. Simpson Dina Demner-Fushman Sameer K. Antani George R. Thoma 《Information Retrieval》2014,17(3):229-264
The images found within biomedical articles are sources of essential information useful for a variety of tasks. Due to the rapid growth of biomedical knowledge, image retrieval systems are increasingly becoming necessary tools for quickly accessing the most relevant images from the literature for a given information need. Unfortunately, article text can be a poor substitute for image content, limiting the effectiveness of existing text-based retrieval methods. Additionally, the use of visual similarity by content-based retrieval methods as the sole indicator of image relevance is problematic since the importance of an image can depend on its context rather than its appearance. For biomedical image retrieval, multimodal approaches are often desirable. We describe in this work a practical multimodal solution for indexing and retrieving the images contained in biomedical articles. Recognizing the importance of text in determining image relevance, our method combines a predominately text-based image representation with a limited amount of visual information, in the form of quantized content-based visual features, through a process called global feature mapping. The resulting multimodal image surrogates are easily indexed and searched using existing text-based retrieval systems. Our experimental results demonstrate that our multimodal strategy significantly improves upon the retrieval accuracy of existing approaches. In addition, unlike many retrieval methods that utilize content-based visual features, the response time of our approach is negligible, making it suitable for use with large collections. 相似文献
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Information Retrieval from Documents: A Survey 总被引：4，自引：0，他引：4
Given the phenomenal growth in the variety and quantity of data available to users through electronic media, there is a great demand for efficient and effective ways to organize and search through all this information. Besides speech, our principal means of communication is through visual media, and in particular, through documents. In this paper, we provide an update on Doermann's comprehensive survey (1998) of research results in the broad area of document-based information retrieval. The scope of this survey is also somewhat broader, and there is a greater emphasis on relating document image analysis methods to conventional IR methods.Documents are available in a wide variety of formats. Technical papers are often available as ASCII files of clean, correct, text. Other documents may only be available as hardcopies. These documents have to be scanned and stored as images so that they may be processed by a computer. The textual content of these documents may also be extracted and recognized using OCR methods. Our survey covers the broad spectrum of methods that are required to handle different formats like text and images. The core of the paper focuses on methods that manipulate document images directly, and perform various information processing tasks such as retrieval, categorization, and summarization, without attempting to completely recognize the textual content of the document. We start, however, with a brief overview of traditional IR techniques that operate on clean text. We also discuss research dealing with text that is generated by running OCR on document images. Finally, we also briefly touch on the related problem of content-based image retrieval. 相似文献
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Medical image retrieval can assist physicians in finding information supporting their diagnosis and fulfilling information needs. Systems that allow searching for medical images need to provide tools for quick and easy navigation and query refinement as the time available for information search is often short. Relevance feedback is a powerful tool in information retrieval. This study evaluates relevance feedback techniques with regard to the content they use. A novel relevance feedback technique that uses both text and visual information of the results is proposed. The two information modalities from the image examples are fused either at the feature level using the Rocchio algorithm or at the query list fusion step using a common late fusion rule. Results using the ImageCLEF 2012 benchmark database for medical image retrieval show the potential of relevance feedback techniques in medical image retrieval. The mean average precision (mAP) is used as the evaluation metric and the proposed method outperforms commonly-used methods. The baseline without feedback reached 16 % whereas the relevance feedback with 20 images reached up to 26.35 % with three steps and when using 100 images up to 34.87 % in four steps. Most improvements occur in the first two steps of relevance feedback and then results start to become relatively flat. This might also be due to only using positive feedback as negative feeback often also improves results after more steps. The effect of relevance feedback in automatically spelling corrected and translated queries is investigated as well. Results without mistakes were better than spell-corrected results but the spelling correction more than double results over non-corrected retrieval. Multimodal relevance feedback has shown to be able to help visual medical information retrieval. Next steps include integrating semantics into relevance feedback techniques to benefit from the structured knowledge of ontologies and experimenting on the fusion of text and visual information. 相似文献
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超文本全文检索系统的研究 总被引：4，自引：3，他引：1
全文检索和超文本技术的结合是情报检索领域的一个发展方向, 但目前已有的全文检索系统都缺乏超文本能力, 而超文本系统也缺乏全文检索功能。本文提出了一个超文本全文检索系统的模型, 并介绍了一个基于该模型的试验系统HFTRS (Hypertext Full Text Retrieval System) , 试图就超文本技术和全文检索技术的结合作一探讨。 相似文献
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[目的/意义] 对比文件是用以判断专利能否授权或无效的重要文件,针对传统信息检索方法的不足且鲜有利用机器学习方法研究对比文件检索的问题,在引入对比文件信息的基础上,构建专利相关性判定模型。[方法/过程] 以专利无效判决书中的目标专利与对比文件为数据集进行实验,提取文本相似度、共现词汇和共词数量特征信息,利用GBDT模型将对比文件的检索问题转化为判断其是否相关的分类问题。[结果/结论] 研究结果表明,不同字段数据对分类效果的贡献不同,其中说明书字段的准确率、召回率和F1值分别为79%、48%和59%,并且多特征集成后的分类效果显著优于单一文本相似度的结果,最后对实验错分情况进行分析,指出本研究下一步的研究方向。 相似文献