登录
首页 » matlab » k-means+BOF

k-means+BOF

于 2020-11-28 发布 文件大小:11408KB
0 226
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

  • Face-detection-based-on-skin-color
    人脸检测,能够检测出多张人脸,通过建立肤色模型,图像预处理等,分割出肤色区域,效果不错。(Face detection and can detect more than one face, through the establishment of skin color model, image preprocessing, segmentation of color of skin area, the effect is good. )
    2021-03-17 00:49:21下载
    积分:1
  • Segmentation
    虹膜识别的内外边缘图像分割,包括瞳孔的分离,还有外边界的分离(Iris recognition both inside and outside the edge of image segmentation, including the separation of the pupil, as well as outside the borders separating)
    2007-11-27 20:26:06下载
    积分:1
  • 1
    说明:  利用matlab进行数字图像处理,滤除方格形条纹噪声,滤除效果还可以,但需要后期处理。(Digital image processing using matlab, square-shaped stripe noise filter, filter effects can be, but need post-processing.)
    2011-04-10 15:54:27下载
    积分:1
  • fenduanjiaozheng
    图像几何校正算法。采用分段校正方法对图像进行校正。(Image geometric correction algorithm. The use of sub-correction method of image correction.)
    2009-02-17 16:37:27下载
    积分:1
  • SIFT
    针对大尺度图像配准和不同传感器图像配准问题, 介绍了一种基于 SIFT 的图像配准方 法。(For large-scale image registration and image registration problem from different sensors, introduces a SIFT-based image registration method.)
    2013-07-18 15:13:18下载
    积分:1
  • IICandIID_R_nmp
    对特征值进行非极大值抑制的角点检测,并且选用了合适的阈值加强检测效果(corner detection by non-maxima suppression of Eigenvalues and selected the appropriate threshold to enhance detection)
    2013-10-29 11:38:22下载
    积分:1
  • bang_ww55
    采用了小波去噪的思想,已经调试成功.内含m文件,可直接运行,用于建立主成分分析模型。( Using wavelet denoising thought, Has been successful debugging. M contains files can be directly run, Principal component analysis model for establishing.)
    2017-03-25 13:43:45下载
    积分:1
  • aa
    说明:  用matlab做彩色图像的二维直方图,比如rb、rg、bg等(use matlab to draw colour image histogram of two dimension )
    2009-12-10 17:37:00下载
    积分:1
  • DRR_registration
    通过将三维图像投影成2维ddr图像来实现2D-3D图像配准(By three-dimensional image projection into 2-D images ddr to achieve 2D-3D Image Registration)
    2008-01-17 18:34:03下载
    积分:1
  • particle-filter--algorithm-code
    文件中含有粒子滤波完整代码以及几种改进粒子滤波算法代码,运行主函数可以分别看到粒子滤波算法和改进后的算法运行效果,适合初学者学习应用(The file contains the complete code of particle filter and several improved particle filter algorithm code, run the main function algorithm performs the particle filter algorithm and the improved see respectively, suitable for beginners to learn to use )
    2014-04-21 09:50:17下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载