Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
vt::distance::Accumulator< T >Meta-function returning a type that can be used to accumulate many values of T
vt::DatabaseClass for efficiently matching a bag-of-words representation of a document (image) against a database of known documents
vt::DefaultAllocator< Feature >Meta-function to get the default allocator for a particular feature type
vt::DefaultAllocator< Eigen::Matrix< Scalar, Rows, Cols, Options, MaxRows, MaxCols > >
vt::GenericTreeVocabulary tree wrapper for easy integration with OpenCV features, or when the (dense) descriptor size and/or type isn't known at compile time
vt::InitGivenDummy initializer for K-means that leaves the centers as-is
vt::InitRandomInitializer for K-means that randomly selects k features as the cluster centers
vt::distance::L2< Feature >Default implementation of L2 distance metric
vt::distance::L2< cv::Mat >L2 distance specialization for cv::Mat
vt::distance::L2< Eigen::Matrix< Scalar, Rows, Cols, Options, MaxRows, MaxCols > >Specialization for Eigen::Matrix types
vt::MatchStruct representing a single database match
vt::MutableVocabularyTree< Feature, Distance, FeatureAllocator >Vocabulary tree that exposes the hierarchical clustering centers. Mainly intended for building a new tree
vt::SimpleKmeans< Feature, Distance, FeatureAllocator >Class for performing K-means clustering, optimized for a particular feature type and metric
vt::TreeBuilder< Feature, Distance, FeatureAllocator >Class for building a new vocabulary by hierarchically clustering a set of training features
vt::VocabularyTree< Feature, Distance, FeatureAllocator >Optimized vocabulary tree quantizer, templated on feature type and distance metric for maximum efficiency
vt::Database::WordFrequency


vocabulary_tree
Author(s): Patrick Mihelich
autogenerated on Thu Jan 2 2014 12:12:26