| vt::distance::Accumulator< T > | Meta-function returning a type that can be used to accumulate many values of T |
| vt::Database | Class 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::GenericTree | Vocabulary tree wrapper for easy integration with OpenCV features, or when the (dense) descriptor size and/or type isn't known at compile time |
| vt::InitGiven | Dummy initializer for K-means that leaves the centers as-is |
| vt::InitRandom | Initializer 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::Match | Struct 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 | |
