Description: 随着计算机技术和国际互联网的飞速发展，包括图像在内的各种多媒体数据的数量正以惊人的速度增长，人们很容易在多媒体信息海洋中迷失方向，如何从中有效地检索有用信息是一个很关键和迫切的问题。本文回顾了图像检索技术的发展状况，阐述了基于内容的图像检索的关键技术，结合认知心理学模型和智能科学技术，重点探讨了未来图像检索的发展方向和技术路线。 -with computer technology and the Internet's rapid development, include images of various kinds of multimedia data volume is a phenomenal growth rate. it is very easy to multimedia information in the ocean lost direction, How can effectively retrieve useful information is a critical and urgent issue. This paper reviews the image retrieval technology development, elaborated on the content-based image retrieval of key technologies, cognitive psychology and the cognitive model of science and technology, with an emphasis on the image retrieval future direction of development and technical route. Platform: |
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Author:耿上 |
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Description: a simple image retrieval method is presented, based on the color distribution of the images. Platform: |
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Author:sunda |
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Description: 近年来，随着互联网的高速发展，网上的多媒体信息也急剧增加，这些多媒体信息以图像为主。如何从浩瀚的图像数据库中快速、准确地找出所需要的图像，己成为一个备受关注的研究课题。有效地组织、管理和检索大规模的图像数据成为迫切需要解决的问题。于是基于内容的图像检索(Content-Based Image Retrieval: CBIR)作为一个崭新的研究领域出现了。
本课题拟研究、分析彩色图像红、绿、蓝三基色直方图的生成、特征提取和相似度等问题，应用图像的颜色信息—三基色直方图对图像进行检索。针对基于颜色的图像检索，本文采用应用广泛的RGB颜色空间来表示图像的颜色特征，对颜色分量进行等间隔量化并形成特征矢量并对特征矢量进行归一化处理，采用图像均匀分块的方法引入图像中色彩所处的位置信息，用距离度量函数进行图像的相似性匹配。在此基础上实现了基于三基色直方图算法的检索系统。
本文的研究和实践对于促进基于内容的图像数据库检索技术的应用具有一定的参考价值和实践意义。-With the rapid development of Internet, the multimedia information is booming. All this information is mostly images. Effective recognizing, management and searching all these images have been an emergent problem. This has led the rise of a new research and development field: Content-Based Image Retrieval (CBIR).
The topics to research, analysis color images red, green and blue color histogram generation, feature extraction and the similarity of the issues, application of the color image- trichromatic histogram of the image retrieval. Based on the color against the static image retrieval, this paper application of a wide range of RGB color space to indicate the color image features, the color components, such as spacing and quantitative characteristics of a feature vector and a normalization of vector processing, using uniform image block the introduction of the method in which the color image Location information, and distance measuring function similar to the image of the match. On Platform: |
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Author:qichao |
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Description: 在基于内容的图像检索中,图像的标注字能够缩小图像的高级语义和低级视觉内容
之间的差距,并方便检索. 但手工标注费时费力且结果具有主观不一致性,而图像的自动语义标注
能够将图像的视觉特征转化为图像的标注字信息,为用户的使用带来了极大的方便. 本文提出了一
种基于实例的图像自动语义标注方法.-In content2based image retrieval, annotations of image can not only reduce the gap between high2
grade semantics and low2grade visual content, but is also convenient for retrieval. Aswe all know, manual2annota2
tion is time2consuming, strength2consuming, and the annotation resultsmay be subjectively different, while the au2
tomatic image annotation can transform the visual features into annotations. This benefits users a lot. In this paper,
we p ropose an instance2based method for automatic image annotation Platform: |
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Author:张三风 |
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Description: 这是一个基于内容的图像检索小系统，里面有测试图像。-This is a content-based image retrieval small systems, which has test image. Platform: |
Size: 2054144 |
Author:刘靥 |
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Description: This a complete documents I gathered from IEEE, ACM and other various resources for my thesis project, It s well directed into appropriate folders.
Should be a useful resource for anyone looking into Image Processing and Content-Based Image Retrieval.-This is a complete documents I gathered from IEEE, ACM and other various resources for my thesis project, It s well directed into appropriate folders.
Should be a useful resource for anyone looking into Image Processing and Content-Based Image Retrieval. Platform: |
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Author:S.Zixi |
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Description: 介绍基于内容图像识别的相关技术，他是检索技术的前沿，目前还处于初级阶段-We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging
technology. While the last decade laid foundation to such promise, it also paved the way for a large number
of new techniques and systems, got many new people involved, and triggered stronger association of weakly
related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current
decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of
related subfields.We also discuss significant challenges involved in the adaptation of existing image retrieval
techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so
far, we also conjecture what the future may hold for image retrieval research. Platform: |
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Author:武玉阳 |
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Description: 该论文具有内容图像检索功能，根据颜色，形状，纹理以达到搜索的目的。熟练应用c++语言编译代码。-The paper has content image retrieval function, according to color, shape and texture to achieve the purpose of search. Proficient in code c++ compiler. Platform: |
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Author:穆振兴 |
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Description: 毕业设计，基于内容的图像检索，支持的检索特征包括 sift,颜色直方图，灰度矩阵，HU不变矩，边缘方向直方图，检索方法使用K-means和K-D树两种，需要OPENCV支持，运行时请先选定一个文件夹来生成特征库，特征库用access数据库保存，只支持JPG文件-Graduate design, content-based image retrieval, search features, including support sift, color histogram, gray matrix, HU moment invariants, edge direction histogram, retrieval method using the K-means and KD trees are two kinds of needs OPENCV support Please select a runtime folder to generate the feature library, feature library with access database save, only supports JPG files Platform: |
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Author:平天羽 |
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Description: Content-based medical image retrieval is now getting more and more attention in the
world, a feasible and efficient retrieving algorithm for clinical endoscopic images is urgently
required. Methods: Based on the study of single feature image retrieving techniques, including color
clustering, color texture and shape, a new retrieving method with multi-features fusion and relevance
feedback is proposed to retrieve the desired endoscopic images. Results: A prototype system is set
up to evaluate the proposed method’s performance and some evaluating parameters such as the
retrieval precision & recall, statistical average position of top 5 most similar image on various features, etc.
are therefore given. Conclusions: The algorithm with multi-features fusion and relevance feedback
gets more accurate and quicker retrieving capability than the one with single feature image retrieving
technique due to its flexible feature combination and interactive relevance feedback. Platform: |
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Author:gokul/goks |
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Search - content based image retrieval