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k-means+BOF

于 2020-11-28 发布 文件大小:11408KB
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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

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0 个回复

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    3、 请采用学过的图像复原算法(鼓励自己研究新算法),对 blur3.jpg、 sadhna_53_45.bmp和telescope.bmp 图像进行复原处理。(3, please use the learned image restoration algorithm (to encourage their study new algorithm), to blur3.jpg, sadhna_53_45.bmp and telescope.bmp image restoration processing.)
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    积分:1
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    该程序是对原图像进行梯度加掩模的三次分水岭分割(That the procedure was in the original image mask plus three gradient watershed segmentation)
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