文件列表:

.DS_Store (6148, 2017-12-29)
BagOfFeature.cpp (5831, 2017-12-29)
BagOfFeature.h (941, 2017-12-29)
data\bows.yml (5357481, 2017-12-29)
data\dictionary.yml (5460319, 2017-12-29)
genBOW.cpp (924, 2017-12-29)
LICENSE (35147, 2017-12-29)
makefile (909, 2017-12-29)
query.cpp (1789, 2017-12-29)
result.html (589, 2017-12-29)
testImgs\10comp.jpg (61640, 2017-12-29)
testImgs\11comp.jpg (52674, 2017-12-29)
testImgs\1big.jpg (34966, 2017-12-29)
testImgs\big-small.jpg (38435, 2017-12-29)
train.cpp (1021, 2017-12-29)
data (0, 2019-03-15)
testImgs (0, 2019-03-15)

# Bow-image-retrieval Bow-image-retrieval Branch:SIFTDetector+SIFTExtractor # Created by luminglin on 2017.11.17 1.extract key points and descriptors 2.generate BOWs for each image using SIFT detector and SIFT descriptors. 3.complete idf computation. # Steps to use You need to install opencv and make sure the makefile is no problem. And get the images set from http://wang.ist.psu.edu/~jwang/test1.tar, tar -zxvf to your local dir. vi train.cpp and replace "/Users/lml/Desktop/image.orig/" to your local dir. Then try `make`... We can get 3 output files: train genBOW query There are 3 steps to do now. (1) `./train 1000` train the dictionary with k=1000 (2) `./genBOW /Users/lml/Desktop/image.orig/` generate the BOW Mat for every image, including the idf. (3) `./query /Users/lml/Desktop/image.orig/999.jpg` Now, you can get the result in ./result.html, open result.html, you can get the rank result. # Useful links https://github.com/willard-yuan/image-retrieval/tree/master/bag-of-words-dev-version https://github.com/psastras/vocabtree

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