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CNN-for-Image-Retrieval-master

于 2019-05-05 发布 文件大小:2974KB
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代码说明：

  基于内容的CNN图像检索，编写语言为matlab(CNN Image Retrieval, matlab)

文件列表：

CNN-for-Image-Retrieval-master, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\.gitignore, 6 , 2017-10-07
CNN-for-Image-Retrieval-master\README.md, 5048 , 2017-10-07
CNN-for-Image-Retrieval-master\compute_MAP.m, 1982 , 2017-10-07
CNN-for-Image-Retrieval-master\database, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\database\001_accordion_image_0001.jpg, 25120 , 2017-10-07
CNN-for-Image-Retrieval-master\database\001_accordion_image_0002.jpg, 18640 , 2017-10-07
CNN-for-Image-Retrieval-master\database\001_accordion_image_0003.jpg, 25284 , 2017-10-07
CNN-for-Image-Retrieval-master\database\002_anchor_image_0001.jpg, 4945 , 2017-10-07
CNN-for-Image-Retrieval-master\database\002_anchor_image_0002.jpg, 12210 , 2017-10-07
CNN-for-Image-Retrieval-master\database\002_anchor_image_0003.jpg, 23032 , 2017-10-07
CNN-for-Image-Retrieval-master\database\003_ant_image_0001.jpg, 10067 , 2017-10-07
CNN-for-Image-Retrieval-master\database\003_ant_image_0002.jpg, 10756 , 2017-10-07
CNN-for-Image-Retrieval-master\database\003_ant_image_0003.jpg, 22111 , 2017-10-07
CNN-for-Image-Retrieval-master\database\1982.jpg, 8173 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClasses.txt, 39 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified\accordion, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified\accordion\image_0001.jpg, 25120 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified\accordion\image_0002.jpg, 18640 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified\accordion\image_0003.jpg, 25284 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified\anchor, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified\anchor\image_0001.jpg, 4945 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified\anchor\image_0002.jpg, 12210 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified\anchor\image_0003.jpg, 23032 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified\ant, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified\ant\image_0001.jpg, 10067 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified\ant\image_0002.jpg, 10756 , 2017-10-07
CNN-for-Image-Retrieval-master\databaseClassified\ant\image_0003.jpg, 22111 , 2017-10-07
CNN-for-Image-Retrieval-master\extractCNN.m, 1964 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\.gitattributes, 119 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\.gitignore, 877 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\.gitmodules, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\COPYING, 735 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\Makefile, 9326 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\Makefile.mex, 801 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\Makefile.nvcc, 933 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\README.md, 363 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\Makefile, 3862 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\blocks.tex, 20196 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\figures, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\figures\imnet.pdf, 18884 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\figures\pepper.pdf, 702358 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\figures\svg, 0 , 2017-10-07
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CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\fundamentals.tex, 21304 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\geometry.tex, 16543 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\impl.tex, 16705 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\intro.tex, 18899 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\matconvnet-manual.tex, 3773 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\matdoc.py, 7046 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\matdocparser.py, 11108 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\references.bib, 2729 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site\docs, 0 , 2017-10-07
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CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site\docs\css\fixes.css, 2850 , 2017-10-07
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CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site\docs\faq.md, 1490 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site\docs\functions.md, 2862 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site\docs\gpu.md, 1095 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site\docs\index.md, 4410 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site\docs\install-alt.md, 3407 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site\docs\install.md, 8302 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site\docs\js, 0 , 2017-10-07
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CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site\docs\pretrained.md, 14586 , 2017-10-07
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CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site\docs\training.md, 1166 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\doc\site\docs\wrappers.md, 9183 , 2017-10-07
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CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\examples\cifar, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\examples\cifar\cnn_cifar.m, 5246 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\examples\cifar\cnn_cifar_init.m, 3286 , 2017-10-07
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CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\examples\cnn_train.m, 14592 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\examples\cnn_train_dag.m, 11010 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\examples\imagenet, 0 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\examples\imagenet\cnn_imagenet.m, 6984 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\examples\imagenet\cnn_imagenet_camdemo.m, 1677 , 2017-10-07
CNN-for-Image-Retrieval-master\matconvnet-1.0-beta17\examples\imagenet\cnn_imagenet_deploy.m, 5871 , 2017-10-07

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发表评论

0 个回复

  • tuxiangruihua
    对已有图像进行锐化，这里有不同的方法对比，效果非常好，非常显著(The existing image sharpening, there are different methods to contrast, effect is very good, is very significant )
    2013-06-20 21:20:03下载
    积分：1
  • otsu2d
    二维otsu的快速实验算法，具体可参考相应的文献。(Two-dimensional Otsu rapid test algorithm, specific reference to the corresponding literature.)
    2008-05-08 21:47:54下载
    积分：1
  • circlefitting
    最小二乘法拟合圆vc实现，最小二乘法(least squares analysis)是一种 数学 优化 技术，它通过 最小化 误差 的平方和找到一组数据的最佳 函数 匹配。 最小二乘法是用最简的方法求得一些绝对不可知的真值，而令误差平方之和为最小。(least squares fitting round vc realized, least-squares method (least squares analysis) is a mathematical optimization techniques, It error by minimizing the square and a group of data to find the best matching function. Least-squares method is the most simple method to derive absolutely true value unknown, and so the sum of squared errors to the minimum.)
    2020-09-09 10:38:03下载
    积分：1
  • viola_jones
    这个是最成熟的人脸检测算法viola-jones的matlab版本实现，适合搞研究的同学们(a matlab implementation of the viola-jones face dection!)
    2012-02-19 16:13:31下载
    积分：1
  • canny
    一种在二维、三维数据中利用Canny算子检测边缘的边缘检测器(A Canny edge detector for two-dimensional image and three-dimensional volume data.)
    2021-04-22 15:28:48下载
    积分：1
  • figure-of-merit-
    评价图像边界的好坏，FOM值越趋于1质量越好。(figure of merit to objectively evaluate the performance of edge detectors)
    2011-12-10 13:24:39下载
    积分：1
  • ImgZoom
    OPENCV做图像变形的经典代码啊，适合比较初级的同学看看(OPENCV do image warping the classic code ah, suitable for elementary students to see more)
    2009-12-02 20:06:13下载
    积分：1
  • twomodegauss
    计算一个已归一化到单位区域的双峰值高斯函数(Calculation of a normalized unit area to the two-peak Gaussian function)
    2008-01-10 11:16:45下载
    积分：1
  • Texture
    计算四个方向的灰度共生矩阵，将其作为纹理特征，是一种纹理特征提取算法。(Calculation of the four directions of Gray Level Co-occurrence matrix as texture features, is a kind of texture feature extraction algorithm.)
    2008-03-11 19:51:54下载
    积分：1
  • tw
    说明：  去除图像中的横向条纹，能有效恢复出原始图像(remove images of horizontal stripes, can be effectively restored from the original image)
    2007-06-10 14:00:17下载
    积分：1
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