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Description: 包括词法分析语法分析语义分析(附有源代码)-including lexical analysis syntax analysis semantic analysis (with source code)
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Size: 1164288 |
Author: sky |
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Description: 介绍一种应用对象语义进行图像检索的新方法(应用对象语义进行图像检索的新方法)-Introduce an application object semantic image retrieval of the new method (Application object semantic image retrieval of the new method)
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Size: 169984 |
Author: gs |
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Description: 基于纹理语义特征的图像检索研究.很详细.在其他网站找不到-Semantic features based on texture image retrieval research. In great detail. Can not find in other sites
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Size: 691200 |
Author: sss |
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Description: 语义图像检索，是一个比较有发展潜力的方向，目前国内已经有许多高校开展了研究~-Semantic image retrieval, is a more promising direction, currently there are many colleges and universities have launched a study of ~
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Size: 215040 |
Author: zhong |
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Description: 基于不同特征的图像检索技术，颜色、纹理、形状、空间关系等特征提取的基础上，基于语义的图像检索技术-Different characteristics based image retrieval, color, texture, shape, spatial relations on the basis of feature extraction, image retrieval based on semantic
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Size: 407552 |
Author: 王 克 |
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Description: 基于高层语义的图像检索算法，文件为pdf文档，很好很新的资料。-High-level semantic-based image retrieval algorithm, a document for pdf documents, new information is very good.
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Size: 256000 |
Author: singro jiang |
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Description: 一个快速的图像分割算法，该算法速度非常快，并且能识别纹理，所以分割结果很具语义性。-A fast image segmentation algorithm is very fast, and can identify the texture, so the result is a segmentation semantic.
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Size: 21504 |
Author: Tao |
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Description: 随着数字图象技术、宽带网络技术和数字存储设备技术的发展，在网络上存储、传输大规模分布式数字图象库
成为可能，因此研究基于内容的图象检索技术成为近几年的热点。实现基于内容的图象检索系统的关键问题是实现图象
的语义分割。该文分六类对现有的图象语义分割技术进行了全面的总结，为进一步研究基于内容的图象检索技术奠定了
基础。-With the digital image technology, broadband networks and digital storage device technology, network storage, transmission of large-scale distributed digital image library has become possible, the study of content-based image retrieval technology to become hot in recent years . The realization of content-based image retrieval systems, the key question is to achieve semantic image segmentation. The six points of the existing semantic image segmentation technology to conduct a comprehensive summing up, for further study of content-based image retrieval technology laid the foundation.
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Size: 140288 |
Author: 焦亚民 |
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Description: This approach addresses two difficulties simultaneously: 1)
the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in
monocular aerial images. With the suggested method building outlines can be detected
faster than the mobile robot can explore the area by itself, giving the robot an ability to
“see” around corners. At the same time, the approach can compensate for the absence
of elevation data in segmentation of aerial images. Our experiments demonstrate that
ground-level semantic information (wall estimates) allows to focus the segmentation of
the aerial image to find buildings and produce a ground-level semantic map that covers
a larger area than can be built using the onboard sensors.-This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings inmonocular aerial images. With the suggested method building outlines can be detectedfaster than the mobile robot can explore the area by itself, giving the robot an ability to
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Size: 420864 |
Author: 焦亚民 |
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Description: 本文提出了一种在获取图像低层视觉特征的基础上，利用语义网络对
图像进行语义自动分类，并在此基础上引入相关反馈技术，使图像的低级
物理特征和高级语义特征联系起来的算法，通过人一机协同工作，来弥补
计算机理解能力的不足，不断提高检索效果。
-In this paper, a low-level access to images based on visual characteristics, the use of semantic networks semantic image automatic classification, and on this basis the introduction of relevance feedback techniques, so that low-level image features and physical characteristics of high-level semantic link algorithm, through a machine to work together to make up for the lack of computer ability, continuously improve the search results.
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Size: 5269504 |
Author: 张慧云 |
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Description: 这是关于语义图像检索进展的一片论文，很好的综述性文章，希望研究图像语义的朋友一起学习- Semantic image retrieval has been a crux to bridge“semantic gap”between the simple
visual features and the abundant semantics delivered by an image. The state of the art of semantic
image retrieval is given from three aspects: schemes of image semantic description, approaches to
image semantic processing, and design for image retrieval system
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Size: 100352 |
Author: 张三风 |
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Description: 用于文本分析和分类的pLSA(Probability Latent Semantic Analysis)的Matlab算法，含有测试数据及算法原理介绍。也可用于图像分析。相比原来的版本，增加了示例程序demo.m，以实现可视化效果，用户更容易使用和理解pLSA算法。-For text analysis and classification of pLSA (Probability Latent Semantic Analysis) of the Matlab algorithm with test data and algorithm theory description. Can also be used for image analysis. Compared to the original version, an increase of sample programs demo.m, to achieve the visual effect, the user can more easily use and understand the algorithm.
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Size: 302080 |
Author: Tang Ketan |
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Description: 斯坦福的牛人写的基于语义的图像分割，发表宰了ICCV2009上，很值得参考。-Stanford' s cattle were written based on semantic image segmentation, published slaughtered ICCV2009 on worth considering.
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Size: 1001472 |
Author: 苏航 |
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Description: 一篇EI英文论文，有关图像检索语义方面的。-a novel semantic image retrieval methd
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Size: 3555328 |
Author: guojuan |
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Description: 这儿是关于图像检索的资料和代码。也有我自己写的程序。关于聚类的
-Here is information on image retrieval and code. Have to write my own program. About Clustering
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Size: 6782976 |
Author: 韩昌刚 |
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Description: 一种新的图像中目标提取方法，文章中有实验结果-A new image object extraction method, the article results in
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Size: 930816 |
Author: 董瑞 |
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Description: 一种基于期望最大化( E M) 算法的局部图像特征的语义提取方法。首先提取图像的局部图像特 征， 统计特征在视觉词汇本中的出现频率， 将图像表示成词袋模型； 引入文本分析中的潜在语义分析技术建立从低层图像
特征到高层图像语义之间的映射模型； 然后利用 E M 算法拟合概率模型， 得到图像局部特征的潜在语义概率分布； 最后利 用该模型提取出的图像在潜在语义上的分布来进行图像分析和理解。-Latent Semantic probability distribution using the EM algorithm to fit a probability model, the image characteristics Finally, the distribution of the model to extract the image on the latent semantic image analysis and understanding.
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Size: 8192 |
Author: liushurong |
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Description: CVPR2012_oral
Weakly Supervised Structured Output Learning for Semantic Segmentation-We address the problem of weakly supervised semantic
segmentation. The training images are labeled only by the
classes they contain, not by their location in the image. On
test images instead, the method must predict a class label
for every pixel. Our goal is to enable segmentation algorithms
to use multiple visual cues in this weakly supervised
setting, analogous to what is achieved by fully supervised
methods. However, it is difficult to assess the relative usefulness
of different visual cues from weakly supervised training
data. We define a parametric family of structured models,
where each model weighs visual cues in a different way. We
propose a Maximum Expected Agreement model selection
principle that evaluates the quality of a model from the family
without looking at superpixel labels. Searching for the
best model is a hard optimization problem, which has no
analytic gradient and multiple local optima. We cast it as
a Bayesian optimization problem and propose an
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Size: 2200576 |
Author: 费炳超 |
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Description: 基于内容的图像检索 基于颜色、纹理、形状的图像检索
基于区域的图像检索
基于语义的图像检索
相关反馈
-Based on the content-based image retrieval based on color, texture, shape-based image retrieval region-based image retrieval based on semantic image retrieval relevance feedback
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Size: 5519360 |
Author: liuyi |
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Description: 文章主要介绍了分层语义描述的概念及其在图像理解中的应用-This paper mainly introduces the concept of hierarchical semantic description of the image Comprehension
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Size: 2094080 |
Author: 李超 |
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