VPTree

• vptree

• VPTree Code - Javascript

• move-mirror

• Computational biology - Searching genome datasets for cancer diagnosis

• Image processing - Content based image indexing

• Databases - Indexing and searching in multimedia databases

• Computer vision - Finding similar patches of images

• Music - Speeding melody search in musical databases

• Algorithms - Optimization of evolutionary algorithms

• Original paper by Peter Yianilos

A vantage-point tree recursively splits data into two categories: those that are closer to some vantage-point than the threshold, and those that are farther away. This recursive sorting creates a tree data structure that can be traversed.

using demo

const similarity = require('compute-cosine-similarity');
const VPTreeFactory = require('vptree');

const poseData = [ […], […], […], …] // an array with all the images’ pose data
let vptree ; // where we’ll store a reference to our vptree

// Function from the previous section covering cosine distance
function cosineDistanceMatching(poseVector1, poseVector2) {
    let cosineSimilarity = similarity(poseVector1, poseVector2);
    let distance = 2 * (1 - cosineSimilarity);
    return Math.sqrt(distance);
}

function buildVPTree() {
  // Initialize our vptree with our images’ pose data and a distance function
  vptree = VPTreeFactory.build(poseData, cosineDistanceMatching);
}

findMostSimilarMatch(userPose) {
  // search the vp tree for the image pose that is nearest (in cosine distance) to userPose
  let nearestImage = vptree.search(userPose);

  console.log(nearestImage[0].d) // cosine distance value of the nearest match

  // return index (in relation to poseData) of nearest match. 
  return nearestImage[0].i; 
}

// Build the tree once
buildVPTree();

// Then for each input user pose
let currentUserPose = [...] // an L2 normalized vector representing a user pose. 34-float array (17 keypoints x 2).  
let closestMatchIndex = findMostSimilarMatch(currentUserPose);
let closestMatch = poseData[closestMatchIndex];