登录
首页 » matlab » k-means+BOF

k-means+BOF

于 2020-11-28 发布 文件大小:11408KB
0 243
下载积分: 1 下载次数: 14

代码说明:

  提取sift特征,通过K均值聚类形成特征包,进行图像检索。(SIFT features are extracted and image packets are retrieved through K mean clustering.)

文件列表:

k-means%2BBOF, 0 , 2018-05-14
k-means%2BBOF\do_database.m, 30791 , 2015-09-30
k-means%2BBOF\do_demo.m, 1517 , 2018-04-19
k-means%2BBOF\do_descriptor.m, 6482 , 2015-08-27
k-means%2BBOF\do_diffofg.m, 464 , 2012-09-27
k-means%2BBOF\do_eucidean_distance.m, 304 , 2016-04-13
k-means%2BBOF\do_extrefine.m, 4368 , 2012-11-05
k-means%2BBOF\do_gaussian.m, 3029 , 2012-10-26
k-means%2BBOF\do_localmax.m, 2261 , 2012-11-13
k-means%2BBOF\do_orientation.m, 2765 , 2015-08-22
k-means%2BBOF\do_sift.m, 4493 , 2015-10-09
k-means%2BBOF\get_countVectors.m, 676 , 2016-04-13
k-means%2BBOF\get_sifts.m, 713 , 2016-04-13
k-means%2BBOF\get_singleVector.m, 460 , 2016-04-13
k-means%2BBOF\img_paths.txt, 4447 , 2018-04-19
k-means%2BBOF\K_Means.m, 839 , 2016-04-13
k-means%2BBOF\SIFT_feature, 0 , 2018-05-14
k-means%2BBOF\SIFT_feature\._.DS_Store, 4096 , 2015-10-07
k-means%2BBOF\SIFT_feature\._do_database.m, 4096 , 2015-10-07
k-means%2BBOF\SIFT_feature\._do_descriptor.m, 4096 , 2015-10-07
k-means%2BBOF\SIFT_feature\._do_sift.m, 4096 , 2015-10-07
k-means%2BBOF\SIFT_feature\.DS_Store, 6148 , 2015-09-02
k-means%2BBOF\SIFT_feature\demo-data, 0 , 2018-05-14
k-means%2BBOF\SIFT_feature\demo-data\1.jpg, 5524 , 2012-10-17
k-means%2BBOF\SIFT_feature\demo-data\2.jpg, 5571 , 2012-10-17
k-means%2BBOF\SIFT_feature\demo-data\5.jpg, 35129 , 2012-10-17
k-means%2BBOF\SIFT_feature\demo-data\6.jpg, 34931 , 2012-10-17
k-means%2BBOF\SIFT_feature\demo-data\7.jpg, 9539 , 2012-10-17
k-means%2BBOF\SIFT_feature\demo-data\beaver11.bmp, 189956 , 2012-09-27
k-means%2BBOF\SIFT_feature\demo-data\beaver13.bmp, 189956 , 2012-09-27
k-means%2BBOF\SIFT_feature\demo-data\einstein.pgm, 65596 , 2012-08-15
k-means%2BBOF\SIFT_feature\demo-data\GML_RANSAC_Matlab_Toolbox_0[1].2.rar, 19215 , 2015-08-19
k-means%2BBOF\SIFT_feature\demo-data\harrisandransac.rar, 446099 , 2015-08-19
k-means%2BBOF\SIFT_feature\demo-data\image068.JPG, 14060 , 2012-09-27
k-means%2BBOF\SIFT_feature\demo-data\image069.JPG, 13579 , 2012-09-27
k-means%2BBOF\SIFT_feature\demo-data\image1.jpg, 240943 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image10.jpg, 63924 , 2015-08-21
k-means%2BBOF\SIFT_feature\demo-data\image11.jpg, 145849 , 2015-08-21
k-means%2BBOF\SIFT_feature\demo-data\image2.jpg, 393897 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image3.jpg, 613687 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image4.jpg, 659244 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image5.jpg, 403386 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image6.jpg, 36967 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image7.jpg, 48612 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\image8.jpg, 92051 , 2015-08-18
k-means%2BBOF\SIFT_feature\demo-data\replace1.jpg, 2466289 , 2013-07-01
k-means%2BBOF\SIFT_feature\demo-data\replace2.jpg, 2812145 , 2013-07-01
k-means%2BBOF\SIFT_feature\demo-data\view01.png, 578897 , 2012-09-27
k-means%2BBOF\SIFT_feature\demo-data\view02.png, 574557 , 2012-09-27
k-means%2BBOF\SIFT_feature\do_database.m, 30791 , 2015-09-30
k-means%2BBOF\SIFT_feature\do_descriptor.m, 6482 , 2015-08-27
k-means%2BBOF\SIFT_feature\do_diffofg.m, 464 , 2012-09-27
k-means%2BBOF\SIFT_feature\do_extrefine.m, 4368 , 2012-11-05
k-means%2BBOF\SIFT_feature\do_gaussian.m, 3029 , 2012-10-26
k-means%2BBOF\SIFT_feature\do_localmax.m, 2261 , 2012-11-13
k-means%2BBOF\SIFT_feature\do_orientation.m, 2765 , 2015-08-22
k-means%2BBOF\SIFT_feature\do_sift.m, 4493 , 2015-10-09
k-means%2BBOF\SIFT_feature\smooth.m, 243 , 2012-11-13
k-means%2BBOF\SIFT_feature\util, 0 , 2018-05-14
k-means%2BBOF\SIFT_feature\util\appendimages.m, 359 , 2012-09-27
k-means%2BBOF\SIFT_feature\util\plotsiftframe.m, 1812 , 2012-09-27
k-means%2BBOF\SIFT_feature\util\plotss.m, 640 , 2015-07-31
k-means%2BBOF\SIFT_feature\util\tightsubplot.m, 1859 , 2012-09-27
k-means%2BBOF\smooth.m, 243 , 2012-11-13
k-means%2BBOF\sourcePictures, 0 , 2018-05-14
k-means%2BBOF\sourcePictures\1.jpg, 18138 , 2018-04-14
k-means%2BBOF\sourcePictures\10.jpg, 9506 , 2018-04-14
k-means%2BBOF\sourcePictures\100.jpg, 9568 , 2018-04-15
k-means%2BBOF\sourcePictures\101.jpg, 15883 , 2018-04-15
k-means%2BBOF\sourcePictures\102.jpg, 5979 , 2018-04-15
k-means%2BBOF\sourcePictures\103.jpg, 4686 , 2018-04-15
k-means%2BBOF\sourcePictures\104.jpg, 24421 , 2018-04-15
k-means%2BBOF\sourcePictures\105.jpg, 25652 , 2018-04-15
k-means%2BBOF\sourcePictures\106.jpg, 9463 , 2018-04-15
k-means%2BBOF\sourcePictures\107.jpg, 19874 , 2018-04-15
k-means%2BBOF\sourcePictures\108.jpg, 5267 , 2018-04-15
k-means%2BBOF\sourcePictures\109.jpg, 18393 , 2018-04-15
k-means%2BBOF\sourcePictures\11.jpg, 6031 , 2018-04-14
k-means%2BBOF\sourcePictures\110.jpg, 5664 , 2018-04-15
k-means%2BBOF\sourcePictures\12.jpg, 7202 , 2018-04-14
k-means%2BBOF\sourcePictures\13.jpg, 5459 , 2018-04-14
k-means%2BBOF\sourcePictures\14.jpg, 16511 , 2018-04-14
k-means%2BBOF\sourcePictures\15.jpg, 16722 , 2018-04-14
k-means%2BBOF\sourcePictures\16.jpg, 17399 , 2018-04-14
k-means%2BBOF\sourcePictures\17.jpg, 18570 , 2018-04-14
k-means%2BBOF\sourcePictures\18.jpg, 21290 , 2018-04-14
k-means%2BBOF\sourcePictures\19.jpg, 8726 , 2018-04-14
k-means%2BBOF\sourcePictures\2.jpg, 18123 , 2018-04-14
k-means%2BBOF\sourcePictures\20.jpg, 15315 , 2018-04-14
k-means%2BBOF\sourcePictures\21.jpg, 16620 , 2018-04-14
k-means%2BBOF\sourcePictures\22.jpg, 10571 , 2018-04-14
k-means%2BBOF\sourcePictures\23.jpg, 3279 , 2018-04-14
k-means%2BBOF\sourcePictures\24.jpg, 15179 , 2018-04-14
k-means%2BBOF\sourcePictures\25.jpg, 4237 , 2018-04-14
k-means%2BBOF\sourcePictures\26.jpg, 16937 , 2018-04-14
k-means%2BBOF\sourcePictures\27.jpg, 8714 , 2018-04-14
k-means%2BBOF\sourcePictures\28.jpg, 6136 , 2018-04-14
k-means%2BBOF\sourcePictures\29.jpg, 30527 , 2018-04-14
k-means%2BBOF\sourcePictures\3.jpg, 16845 , 2018-04-14
k-means%2BBOF\sourcePictures\30.jpg, 31940 , 2018-04-14

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • 3
    采用考虑湍流内外尺度影响的退化模型来恢复大气湍流降质图像,运用包含了湍流内外尺度影响的波结构函数、折射率谱以及薄透镜成像的退化函数导出了新的退化模型。(A new degradation model includling the influence of outer-and inner-scale of turbulence is presented to restore atmospheric turbulence degradation images. )
    2014-01-22 16:49:22下载
    积分:1
  • keypointExtraction
    harris, harris-laplace方法,特征点检测方法总结(harris, harris-laplace method, Feature Point Detection Methods)
    2008-06-25 15:36:00下载
    积分:1
  • LS
    说明:  matalb程序编写横向剪切干涉最小二乘法波前拟合。(matalb programming lateral shearing interferometry method of least squares wavefront fitting.)
    2012-10-17 09:25:14下载
    积分:1
  • image-matching-algorithm
    实现了三种图像匹配算法 1:归一化互相关匹配算法 2:基于Hausdorff距离的图像匹配算法 3:图像不变矩匹配算法(Implements three image matching algorithm 1: normalized cross correlation matching algorithm 2: image matching algorithm based on Hausdorff distance 3: The image matching algorithm invariant moments)
    2021-01-05 17:48:54下载
    积分:1
  • ij-ridgedetection-master
    说明:  提取亚像素精度的中心线的线检测算法,适用于线结构光的中心提取(Line detection algorithm for extracting sub-pixel centerline is suitable for extracting the centerline of line structured light.)
    2019-03-08 16:35:05下载
    积分:1
  • Jpeg_ImageProcess
    说明:  本程序在EVC4.0+PPC SDK 2003下调试通过。用Independent JPEG Group发行的JpegLib进行Jpeg图像的读取与保存。(This procedure in EVC4.0+ PPC SDK 2003 adopted under the debugger. By Independent JPEG Group issued JpegLib to read Jpeg images and preservation.)
    2008-11-18 10:27:54下载
    积分:1
  • k-svd_second
    ksvd 其中包含omp算法,对一副自然图像进行稀疏重构,重新形成该图像,找出稀疏系数。(ksvd method.if you want konw more detail please translate the above Chinese words.)
    2014-06-17 17:04:24下载
    积分:1
  • Openimagefile
    在文档下打开一个位图文件,重新绘制,放大并使其二值化显示,()
    2007-08-29 23:23:41下载
    积分:1
  • MATLAB
    常规的中值滤波器,在噪声的密度不是很大的情况下效果不错。但是当概率出现的概率较高时,常规的中值滤波处理后,仍然具有噪声点,并丢失了细节和边缘,效果不是很好。 自适应中值滤波器可以尽可能的保护图像中细节信息,避免图像边缘的细化或者粗化。(The conventional median filter works well when the noise density is not very large. However, when the probability of probability is high, the conventional median filter still has noise points, and the details and edges are missing. The result is not very good. The adaptive median filter can protect details in the image as far as possible and avoid thinning or coarsening of the image edges.)
    2018-05-28 11:29:19下载
    积分:1
  • IFC读写代码
    IFC文件格式读取和写出源码,目前该源码是1.0版本(IFC file format reads and writes the source code, which is currently version 1.0)
    2020-09-16 14:17:55下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载