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Package proposal

This will be a cleaned-up, generic implementation of Nister vocabulary trees intended to replace the one in place_recognition. Problems with the existing version:

Vocabulary Tree

Unlike the current all-in-one class, the new VocabularyTree is responsible for exactly one task: quantizing features into "words."

   1 typedef uint32_t Word;
   3 template<class Feature, class Distance = L2>
   4 class VocabularyTree
   5 {
   6   VocabularyTree(Distance d = Distance());
   8   Word quantize(const Feature& f) const;
  10   void save(const std::string& file) const;
  11   void load(const std::string& file);
  12 };


The Database stores "documents" (images represented as words). It can find the set of documents best matching a query. Internally, it keeps an inverted file for each word mapping that word to the documents containing it.

   1 typedef uint32_t DocId;
   2 typedef std::vector<Word> Document;
   4 class Database
   5 {
   6   // Insert a new document. The returned DocId identifies it.
   7   DocId insert(const Document& words);
   9   struct Match
  10   {
  11     DocId id;
  12     float score;
  13   };
  14   typedef std::vector<Match> Matches;
  16   // Find the top N matches in the database for the query document.
  17   void find(const Document& words, size_t N, Matches& matches) const;
  19   // Find the top N matches AND insert the query document. insert and find
  20   // are essentially the same operation, so this is twice as fast as doing
  21   // them sequentially.
  22   DocId findAndInsert(const Document& words, size_t N, Matches& matches);
  24   // Compute the TF-IDF weights of all the words. To be called after inserting
  25   // a corpus of training examples into the database.
  26   void computeWeights();
  28   void saveWeights(const std::string& file) const;
  29   void loadWeights(const std::string& file);
  30 };


place_recognition already contains a rather fast implementation of K-means. It uses Elkan's algorithm, which greatly reduces the number of distance computations while producing the same results as standard K-means at each iteration. It can use the k-means++ seeding algorithm, which tends to reduce the number of iterations required for convergence. It is already templated to work with float or double features.

Needed changes:

   1 template<class Feature, class Distance>
   2 class Kmeans
   3 {
   4   Kmeans(Distance d = Distance());
   6   // random, use given, kmeans++, ...
   7   void setInitMethod(...);
   9   // Stop after convergence or some number of iterations.
  10   void setMaxIterations(size_t iters);
  12   // Number of times to run the algorithm, if seeding is non-deterministic.
  13   void setStarts(size_t starts);
  15   // Cluster a set of features into k centers, with each input feature
  16   // assigned membership to some center. Returns the error.
  17   distance_type cluster(const std::vector<Feature>& features,
  18                         size_t k,
  19                         std::vector<Feature>& centers,
  20                         std::vector<size_t>& membership) const;
  21 };

A reusable HierarchicalKmeans class would be nice to have also, but requires a more complicated interface. That can wait I think.

Feature concept

Features must have a value type (e.g. float, uint8_t) and dimension (e.g. 176 for Calonder) known at compile time. This information can be accessed through type traits. Normally a feature class will simply be a wrapper for a stack-allocated array, such as fixed-size Eigen vectors or boost::array.

Distance concept

   1 concept Distance
   2 {
   3   typedef ... value_type;  // the coordinate type of the feature
   4   typedef ... result_type; // capable of accumulating value_type
   6   // Returns the (squared) distance between a and b.
   7   result_type operator() (const Feature& a, const Feature& b) const;
   8 };

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2024-06-22 14:21