登录
首页 » matlab » k-means+BOF

k-means+BOF

于 2020-11-28 发布 文件大小:11408KB
0 218
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

  • quanjingtupinheyuchuli
    一种有效的全景图拼合预处理算法.适用于图像的拼接。是论文!(An effective pre-processing algorithm together panorama. Splicing applied to images. Is a thesis!)
    2008-04-15 16:42:03下载
    积分:1
  • hough_circle
    说明:  通过hough变换求图像中的圆的参数(圆心坐标和半径)(Through the hough transform for image circle parameters (center coordinates and radius))
    2008-09-14 09:27:20下载
    积分:1
  • face
    人脸识别matlab源代码,应用主分量分析(PCA)实现了人脸识别。(Face recognition matlab source code, application of principal component analysis (PCA) to achieve a face recognition.)
    2010-02-28 00:45:22下载
    积分:1
  • Edge-extraction-
    图像处理中边缘提取几种方法的matlab代码(many edge extraction codes about image processing)
    2012-06-04 20:40:23下载
    积分:1
  • eye-gaze-estimation
    文中主要研究使用普通的摄像头进行人眼视线方向的估计。在综合介绍了各种人眼视线检测方法的基础上提出了一种新的图像处理的方法来检测人眼视线方向(The main study used an ordinary camera line of sight of the human eye is estimated. In the consolidated a variety of the human eye sight detection method based on a new image processing method to detect the line of sight of the human eye)
    2012-07-10 21:37:32下载
    积分:1
  • gmm
    说明:  可用于图像分割、识别分类、语音识别等的代码实现(Recognition classification)
    2019-12-18 14:42:05下载
    积分:1
  • 2stepss
    摄象机标定的两步法修改程序代码,标定摄象机内外参数(Two-step camera calibration procedure to amend the code, both within and outside the parameters of camera calibration)
    2008-03-21 19:24:11下载
    积分:1
  • Light-Spot
    针对空间发光点缩成光斑图像中心的高精度提取问题,本文提出一种亚像素提取方法。该方法简历了具有高斯能量分布的空间发光点的透视投影光斑图像灰度分部数学模型,证明了空间发光点能量中心的透视投影不变性。(Light-emitting points of space shrunk to spot high-precision center of the image extraction problem, we propose a subpixel extraction method. This method resumes a space with Gaussian energy distribution points of perspective projection spot light gray segment mathematical model to prove the energy center of the space light-emitting point perspective projection invariance.)
    2010-12-10 22:56:07下载
    积分:1
  • duibiduzengq
    一种红外图像自适应对比度增强方法的仿真,绝对的真实,看到那些骗下载分的人恶心。需要自适应修改阈值T和A1,A2的值(An adaptive contrast enhancement method of infrared image simulation, absolutely true, see those who cheat download points. Need adaptive modified threshold T and A1, A2)
    2013-07-11 21:43:25下载
    积分:1
  • cloud
    这是一个很全的云模型图像处理,功能全,已运行(This is a good use of the cloud model program, function, good effect, has been in operation)
    2020-12-28 19:09:01下载
    积分:1
  • 696516资源总数
  • 106658会员总数
  • 16今日下载