This section documents OpenCV’s interface to the FLANN library. FLANN (Fast Library for Approximate Nearest Neighbors) is a library that contains a collection of algorithms optimized for fast nearest neighbor search in large datasets and for high dimensional features. More information about FLANN can be found in [Muja2009] .
The FLANN nearest neighbor index class. This class is templated with the type of elements for which the index is built.
Constructs a nearest neighbor search index for a given dataset.
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The method constructs a fast search structure from a set of features using the specified algorithm with specified parameters, as defined by params. params is a reference to one of the following class IndexParams descendants:
LinearIndexParams When passing an object of this type, the index will perform a linear, bruteforce search.
struct LinearIndexParams : public IndexParams { };KDTreeIndexParams When passing an object of this type the index constructed will consist of a set of randomized kdtrees which will be searched in parallel.
struct KDTreeIndexParams : public IndexParams { KDTreeIndexParams( int trees = 4 ); };
 trees The number of parallel kdtrees to use. Good values are in the range [1..16]
KMeansIndexParams When passing an object of this type the index constructed will be a hierarchical kmeans tree.
struct KMeansIndexParams : public IndexParams { KMeansIndexParams( int branching = 32, int iterations = 11, flann_centers_init_t centers_init = CENTERS_RANDOM, float cb_index = 0.2 ); };
 branching The branching factor to use for the hierarchical kmeans tree
 iterations The maximum number of iterations to use in the kmeans clustering stage when building the kmeans tree. A value of 1 used here means that the kmeans clustering should be iterated until convergence
 centers_init The algorithm to use for selecting the initial centers when performing a kmeans clustering step. The possible values are CENTERS_RANDOM (picks the initial cluster centers randomly), CENTERS_GONZALES (picks the initial centers using Gonzales’ algorithm) and CENTERS_KMEANSPP (picks the initial centers using the algorithm suggested in arthur_kmeanspp_2007 )
 cb_index This parameter (cluster boundary index) influences the way exploration is performed in the hierarchical kmeans tree. When cb_index is zero the next kmeans domain to be explored is chosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.
CompositeIndexParams When using a parameters object of this type the index created combines the randomized kdtrees and the hierarchical kmeans tree.
struct CompositeIndexParams : public IndexParams { CompositeIndexParams( int trees = 4, int branching = 32, int iterations = 11, flann_centers_init_t centers_init = CENTERS_RANDOM, float cb_index = 0.2 ); };LshIndexParams When using a parameters object of this type the index created uses multiprobe LSH (by MultiProbe LSH: Efficient Indexing for HighDimensional Similarity Search by Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li., Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB). Vienna, Austria. September 2007)
struct LshIndexParams : public IndexParams { LshIndexParams( unsigned int table_number, unsigned int key_size, unsigned int multi_probe_level ); };
 table_number the number of hash tables to use (between 10 and 30 usually).
 key_size the size of the hash key in bits (between 10 and 20 usually).
 multi_probe_level the number of bits to shift to check for neighboring buckets (0 is regular LSH, 2 is recommended).
AutotunedIndexParams When passing an object of this type the index created is automatically tuned to offer the best performance, by choosing the optimal index type (randomized kdtrees, hierarchical kmeans, linear) and parameters for the dataset provided.
struct AutotunedIndexParams : public IndexParams { AutotunedIndexParams( float target_precision = 0.9, float build_weight = 0.01, float memory_weight = 0, float sample_fraction = 0.1 ); };
 target_precision Is a number between 0 and 1 specifying the percentage of the approximate nearestneighbor searches that return the exact nearestneighbor. Using a higher value for this parameter gives more accurate results, but the search takes longer. The optimum value usually depends on the application.
 build_weight Specifies the importance of the index build time raported to the nearestneighbor search time. In some applications it’s acceptable for the index build step to take a long time if the subsequent searches in the index can be performed very fast. In other applications it’s required that the index be build as fast as possible even if that leads to slightly longer search times.
 memory_weight Is used to specify the tradeoff between time (index build time and search time) and memory used by the index. A value less than 1 gives more importance to the time spent and a value greater than 1 gives more importance to the memory usage.
 sample_fraction Is a number between 0 and 1 indicating what fraction of the dataset to use in the automatic parameter configuration algorithm. Running the algorithm on the full dataset gives the most accurate results, but for very large datasets can take longer than desired. In such case using just a fraction of the data helps speeding up this algorithm while still giving good approximations of the optimum parameters.
SavedIndexParams This object type is used for loading a previously saved index from the disk.
struct SavedIndexParams : public IndexParams { SavedIndexParams( std::string filename ); };
 filename The filename in which the index was saved.
Performs a Knearest neighbor search for a given query point using the index.
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Performs a radius nearest neighbor search for a given query point.
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Saves the index to a file.
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Returns the index parameters.
The method is useful in the case of autotuned indices, when the parameters are chosen during the index construction. Then, the method can be used to retrieve the actual parameter values.