Originally created by Mohamed Aly at http://vision.caltech.edu/malaa/software/research/image-search/
--This is a modification of origial source to get it sucessfully compiled with MATLAB 2015 and VS2013. --Few cpp files were modified and new functions were added to get it compiled successfully with MATLAB. --boost libraries are added into source folder from www.boost.org/
============================================================================= How to use:
- You need MATLAB with Visual Studio installed on your system.Else install it as follows: Install Microsoft Visual Studio 2013. Install Matlab 2015
- Check for MATLABs configuration with VS using "mex -setup" in command prompt if not then configure it.
- Run "Compile.m" from folder,it will show success message after compilation
- Run "Demo.m" to check working of new compiled files.
CALTECH LARGE SCALE IMAGE SEARCH TOOLBOX
=============================================================================
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ DESCRIPTION +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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:
- 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).
- Min-Hash.
- Full Representation:
The images are represented by the individual features.
It includes algorithms for fast approximate nearest neighbor search:
- Kd-Trees (Kdt).
- Hierarchical K-Means (Hkm).
- Locality Senstivie Hashing (LSH), with several hash functions:
** Hamming hash function (bit sampling, approximates hamming distance) i.e.
h = x[i]
** Cosine hash function (random hyperplanes through the origin, approximates
dot product) i.e. h = sign(<x,r>)
** L1 hash function (approximates the L1 distance) i.e. h = floor((x[i]-b) / w)
** L2 hash function (random hyperplanes with bias, approximates euclidean distance, similar to E2LSH) i.e. h = floor((<x,r> - b) / w) ** Spherical Simplex (approximates distances on the unit hypersphere) ** Spherical Orthoplex (approximates distances on the unit hypersphere) ** Spherical Hypercube (approximates distances on the unit hypersphere) ** Binary Gausian Kernels (approximates gaussian kernel)
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ CONTENTS +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
ccvAkmeansClean.m: clears an AKM dictionary from memory ccvAkmeansCreate.m: creates an AKM dictionary ccvAkmeansLookup.m: looks up words in an AKM dictionary
ccvBowGetDict.m: creates a dictionary of visual words. Supports AKM & HKM. ccvBowGetWordsClean.m: clears a dictionary from memory. ccvBowGetWordsInit.m: initializes a dictionary to lookup a sequence of images. ccvBowGetWords.m: looks up words in a dictionary. The typical sequence is to call ccvBowGetWordsInit at the start, then call ccvBowGetWords in a loop for different images, and finally call ccvBowGetWordsClean to clear it from memory.
ccvDistance.m: computes distances between pairs of point sets.
ccvHkmClean.m: clears an HKM structure from memory. ccvHkmCreate.m: creats and HKM structure. ccvHkmExport.m: exports an HKM structure to Matlab. ccvHkmImport.m: imports and HKM structure form Matlab. ccvHkmKnn.m: performs k-nearest neighbor on an HKM structure. ccvHkmLeafIds.m: retrieves the leaf id for input points. used in HKM dictionaries as the visual words.
ccvInvFileClean.m: clears an inverted file from memory ccvInvFileCompStats.m: prepares the inverted file for search operations. ccvInvFileInsert.m: inserts docs in the inverted file ccvInvFileLoad.m: loads an inverted file from a file ccvInvFileSave.m: saves an inverted file to a file ccvInvFileSearch.m: searches through the inverted file
ccvInvFileExtraClean.m: clears an inverted file from memory ccvInvFileExtraCompStats.m: prepares the inverted file for search operations. ccvInvFileExtraInsert.m: inserts docs in the inverted file ccvInvFileExtraSearch.m: searches through the inverted file
ccvKdtClean.m: clears a Kdt structure from memory ccvKdtCreate.m: creates a Kdt ccvKdtKnn.m: performs k-nearest neighbor on the kdt ccvKdtPoints.m: returns the points that share the same leaves without computing distances
ccvKnn.m: performs brute force k-NN
ccvLshBucketId.m: returns the id of the bucket ccvLshBucketPoints.m: returns the points in a given bucket ccvLshClean.m: clears an LSH from memory ccvLshCreate.m: creates an LSH ccvLshFuncVal.m: returns the values of the hash functions ccvLshInsert.m: inserts into the LSH ccvLshKnn.m: performs k-NN ccvLshLoad.m: loads from a file ccvLshSave.m: saves to a file ccvLshSearch.m: returns points in the same bucket without distance computations ccvLshStats.m: returns stats
ccvNormalize.m: normalizes input points ccvNorm.m: returns the norm of the input points
ccvRandSeed.m: sets/restores the random seed
COMPILE.m: compiles the mex files
DEMO.m: demo file
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ INSTALL +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ To install package, unzip the package somewhere:
cd ~ unzip caltech-image-search.zip cd ~/caltech-image-search
Then compile the MEX files with Matlab:
matlab&
COMPILE
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ DEMO +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
See the demo file DEMO.m for example usages.
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ CONTACT +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Mohamed Aly [email protected]
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ REFERENCE +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
[1] Mohamed Aly, Mario Munich, and Pietro Perona. Indexing in Large Scale Image Collections: Scaling Properties and Benchmark. IEEE Workshop on Applications of Computer Vision WACV, January 2011.
Boost libraries has its own licence please visit http://www.boost.org/users/license.html for more info.
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