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Caltech's Large Scale Image Search Toolbox
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============================================================================= 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: 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). * Min-Hash. 2. 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) +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ CHANGES +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Nov. 5, 2010: version 1.0. +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ CONTENTS +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Approximate K-Means -------------------- ccvAkmeansClean.m: clears an AKM dictionary from memory ccvAkmeansCreate.m: creates an AKM dictionary ccvAkmeansLookup.m: looks up words in an AKM dictionary Bag of Words ------------ 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. Distance -------- ccvDistance.m: computes distances between pairs of point sets. Hierarchical K-Means -------------------- 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. Inverted File -------------- 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 Extra Inverted File (Hamming Embedding) ---------------------------------------- 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 Kd-Tree -------- 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 K-Nearest Neighbor ------------------- ccvKnn.m: performs brute force k-NN Locality Sensitive Hashing --------------------------- 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 <malaa at vision d0t caltech d0t edu> +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ REFERENCE +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++  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.