文件名称:caltech-image-search-1.0
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大规模图像检索的代码，matlab与c++混合编程。总结了目前图像检索领域目前主要存在的方法。通过阅读该代码，可以对于经典的“词袋”模型（bow模型）有个具体的了解，但是该代码没有提供前序的特征提取，是直接从对提取好的特征向量聚类开始的，包括了k-means，分层k-means（HKM）聚类，倒排文件的建立和索引等，该代码还提供了局部敏感哈希（LSH）方法。最后，这份代码是下面这篇论文的作者提供的，
Indexing in Large Scale Image Collections: Scaling Properties and Benchmark-This C++/Matlab package implements several algorithms used for large scale
image search. The algorithms are implemented in C++, with an eye on large
scale databases. It can handle millions of images and hundreds of millions
of local features. It has MEX interfaces for Matlab, but can also be used
(with possible future modifications) from Python and directly from C++. It
can also be used for approximate nearest neighbor search, especially using
the Kd-Trees or LSH implementations.
The algorithms can be divided into two broad categories, depending on the
approach taken for image search:
1. Bag of Words:
----------------
The images are represented by histograms of visual words.
It includes algorithms for computing dictionaries:
* K-Means.
* Approximate K-Means (AKM).
* Hierarchical K-Means (HKM).
It also includes algorithms for fast search:
* Inverted File Index.
* Inverted File Index with Extra Information (for example for implementing
Hamming Embedding).
*
Indexing in Large Scale Image Collections: Scaling Properties and Benchmark-This C++/Matlab package implements several algorithms used for large scale
image search. The algorithms are implemented in C++, with an eye on large
scale databases. It can handle millions of images and hundreds of millions
of local features. It has MEX interfaces for Matlab, but can also be used
(with possible future modifications) from Python and directly from C++. It
can also be used for approximate nearest neighbor search, especially using
the Kd-Trees or LSH implementations.
The algorithms can be divided into two broad categories, depending on the
approach taken for image search:
1. Bag of Words:
----------------
The images are represented by histograms of visual words.
It includes algorithms for computing dictionaries:
* K-Means.
* Approximate K-Means (AKM).
* Hierarchical K-Means (HKM).
It also includes algorithms for fast search:
* Inverted File Index.
* Inverted File Index with Extra Information (for example for implementing
Hamming Embedding).
*
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下载文件列表
ccvAkmeansClean.m
ccvAkmeansCreate.m
ccvAkmeansLookup.m
ccvBowGetDict.m
ccvBowGetWordsClean.m
ccvBowGetWordsInit.m
ccvBowGetWords.m
ccvBowSpCheck.m
ccvDistance.m
ccvHkmClean.m
ccvHkmCreate.m
ccvHkmExport.m
ccvHkmImport.m
ccvHkmKnn.m
ccvHkmLeafIds.m
ccvInvFileClean.m
ccvInvFileCompStats.m
ccvInvFileExtraClean.m
ccvInvFileExtraCompStats.m
ccvInvFileExtraInsert.m
ccvInvFileExtraSearch.m
ccvInvFileInsert.m
ccvInvFileLoad.m
ccvInvFileSave.m
ccvInvFileSearch.m
ccvKdtClean.m
ccvKdtCreate.m
ccvKdtKnn.m
ccvKdtPoints.m
ccvKnn.m
ccvLshBucketId.m
ccvLshBucketPoints.m
ccvLshClean.m
ccvLshCreate.m
ccvLshFuncVal.m
ccvLshInsert.m
ccvLshKnn.m
ccvLshLoad.m
ccvLshSave.m
ccvLshSearch.m
ccvLshStats.m
ccvNormalize.m
ccvNorm.m
ccvRandSeed.m
ccvSumIndexed.m
COMPILE.m
DEMO.m
ccCommon.hpp
ccData.hpp
ccDistance.hpp
ccHKmeans.hpp
ccInvertedFileExtra.hpp
ccInvertedFile.hpp
ccKdt.hpp
ccLsh.hpp
ccMatrix.hpp
ccNormalize.hpp
ccVector.hpp
mxCommon.hpp
mxData.hpp
mxDistance.hpp
mxHKmeans.hpp
mxInvFileExtra.hpp
mxInvFile.hpp
mxLsh.hpp
mxMatrix.hpp
mxSumIndexed.hpp
mxVector.hpp
ccDistance.cpp
ccHKmeans.cpp
ccInvertedFile.cpp
ccInvertedFileExtra.cpp
ccKdt.cpp
ccLsh.cpp
ccNormalize.cpp
mxDistance.cpp
mxHkmClean.cpp
mxHkmCreate.cpp
mxHkmExport.cpp
mxHkmImport.cpp
mxHkmKnn.cpp
mxHkmLeafIds.cpp
mxInvFileClean.cpp
mxInvFileCompStats.cpp
mxInvFileExtraClean.cpp
mxInvFileExtraCompStats.cpp
mxInvFileExtraFill.cpp
mxInvFileExtraSearch.cpp
mxInvFileFill.cpp
mxInvFileFillData.cpp
mxInvFileLoad.cpp
mxInvFileSave.cpp
mxInvFileSearch.cpp
mxKdtClean.cpp
mxKdtCreate.cpp
mxKdtKnn.cpp
mxKdtPoints.cpp
mxKnn.cpp
mxLshBucketId.cpp
mxLshBucketPoints.cpp
ccvAkmeansCreate.m
ccvAkmeansLookup.m
ccvBowGetDict.m
ccvBowGetWordsClean.m
ccvBowGetWordsInit.m
ccvBowGetWords.m
ccvBowSpCheck.m
ccvDistance.m
ccvHkmClean.m
ccvHkmCreate.m
ccvHkmExport.m
ccvHkmImport.m
ccvHkmKnn.m
ccvHkmLeafIds.m
ccvInvFileClean.m
ccvInvFileCompStats.m
ccvInvFileExtraClean.m
ccvInvFileExtraCompStats.m
ccvInvFileExtraInsert.m
ccvInvFileExtraSearch.m
ccvInvFileInsert.m
ccvInvFileLoad.m
ccvInvFileSave.m
ccvInvFileSearch.m
ccvKdtClean.m
ccvKdtCreate.m
ccvKdtKnn.m
ccvKdtPoints.m
ccvKnn.m
ccvLshBucketId.m
ccvLshBucketPoints.m
ccvLshClean.m
ccvLshCreate.m
ccvLshFuncVal.m
ccvLshInsert.m
ccvLshKnn.m
ccvLshLoad.m
ccvLshSave.m
ccvLshSearch.m
ccvLshStats.m
ccvNormalize.m
ccvNorm.m
ccvRandSeed.m
ccvSumIndexed.m
COMPILE.m
DEMO.m
ccCommon.hpp
ccData.hpp
ccDistance.hpp
ccHKmeans.hpp
ccInvertedFileExtra.hpp
ccInvertedFile.hpp
ccKdt.hpp
ccLsh.hpp
ccMatrix.hpp
ccNormalize.hpp
ccVector.hpp
mxCommon.hpp
mxData.hpp
mxDistance.hpp
mxHKmeans.hpp
mxInvFileExtra.hpp
mxInvFile.hpp
mxLsh.hpp
mxMatrix.hpp
mxSumIndexed.hpp
mxVector.hpp
ccDistance.cpp
ccHKmeans.cpp
ccInvertedFile.cpp
ccInvertedFileExtra.cpp
ccKdt.cpp
ccLsh.cpp
ccNormalize.cpp
mxDistance.cpp
mxHkmClean.cpp
mxHkmCreate.cpp
mxHkmExport.cpp
mxHkmImport.cpp
mxHkmKnn.cpp
mxHkmLeafIds.cpp
mxInvFileClean.cpp
mxInvFileCompStats.cpp
mxInvFileExtraClean.cpp
mxInvFileExtraCompStats.cpp
mxInvFileExtraFill.cpp
mxInvFileExtraSearch.cpp
mxInvFileFill.cpp
mxInvFileFillData.cpp
mxInvFileLoad.cpp
mxInvFileSave.cpp
mxInvFileSearch.cpp
mxKdtClean.cpp
mxKdtCreate.cpp
mxKdtKnn.cpp
mxKdtPoints.cpp
mxKnn.cpp
mxLshBucketId.cpp
mxLshBucketPoints.cpp