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Titlebook: ImageCLEF; Experimental Evaluat Henning Müller,Paul Clough,Barbara Caputo Book 2010 Springer-Verlag Berlin Heidelberg 2010 Annotation.Image

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书目名称ImageCLEF
副标题Experimental Evaluat
编辑Henning Müller,Paul Clough,Barbara Caputo
视频video
概述Only Book comprehensively describing benchmarks for image retrieval.Written by the organizers of the ImageCLEF evaluation campaign.Includes lots of detailed descriptions of effective retrieval across
丛书名称The Information Retrieval Series
图书封面Titlebook: ImageCLEF; Experimental Evaluat Henning Müller,Paul Clough,Barbara Caputo Book 2010 Springer-Verlag Berlin Heidelberg 2010 Annotation.Image
描述The pervasive creation and consumption of content, especially visual content, is ingrained into our modern world. We’re constantly consuming visual media content, in printed form and in digital form, in work and in leisure pursuits. Like our cave– man forefathers, we use pictures to record things which are of importance to us as memory cues for the future, but nowadays we also use pictures and images to document processes; we use them in engineering, in art, in science, in medicine, in entertainment and we also use images in advertising. Moreover, when images are in digital format, either scanned from an analogue format or more often than not born digital, we can use the power of our computing and networking to exploit images to great effect. Most of the technical problems associated with creating, compressing, storing, transmitting, rendering and protecting image data are already solved. We use - cepted standards and have tremendous infrastructure and the only outstanding ch- lenges, apart from managing the scale issues associated with growth, are to do with locating images. That involves analysing them to determine their content, clas- fying them into related groupings, and searc
出版日期Book 2010
关键词Annotation; Image Retrieval; Medical Image Processing; Multimedia Retrieval; Performance Evaluation; Robo
版次1
doihttps://doi.org/10.1007/978-3-642-15181-1
isbn_softcover978-3-642-26463-4
isbn_ebook978-3-642-15181-1Series ISSN 1871-7500 Series E-ISSN 2730-6836
issn_series 1871-7500
copyrightSpringer-Verlag Berlin Heidelberg 2010
The information of publication is updating

书目名称ImageCLEF影响因子(影响力)




书目名称ImageCLEF影响因子(影响力)学科排名




书目名称ImageCLEF网络公开度




书目名称ImageCLEF网络公开度学科排名




书目名称ImageCLEF被引频次




书目名称ImageCLEF被引频次学科排名




书目名称ImageCLEF年度引用




书目名称ImageCLEF年度引用学科排名




书目名称ImageCLEF读者反馈




书目名称ImageCLEF读者反馈学科排名




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The Wikipedia Image Retrieval Taske available test collections, summarises the retrieval approaches employed by the groups that participated in the task during the 2008 and 2009 ImageCLEF campaigns, provides an analysis of the main evaluation results, identifies best practices for effective retrieval, and discusses open issues.
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The Medical Image Classification Taskclassification schemes working on full–image descriptors that were very successful in 2005. In particular the use of discriminative classification methods such as support vector machines and the use of local image descriptors were empirically shown to be important building blocks for medical image classification.
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Relevance Judgments for Image Retrieval EvaluationeCLEF. We discuss the advantages and limitations of creating pools that are then judged by human experts. Finally, we discuss our experiences with the subjectivity of the relevance process and the relative robustness of the performance measures to variability in relevance judging.
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Object and Concept Recognition for Image Retrievalve the tasks and the lessons learnt are outlined. The results of all years are illustrated, compared and the most promising approaches are highlighted. Finally, the interactions with the photo retrieval task are presented.
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Book 2010dia content, in printed form and in digital form, in work and in leisure pursuits. Like our cave– man forefathers, we use pictures to record things which are of importance to us as memory cues for the future, but nowadays we also use pictures and images to document processes; we use them in engineer
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