Efficient Similarity Search via Sparse Coding

Abstract

This work presents a new indexing method using sparse coding for fast approximate Nearest Neighbors (NN) on high dimensional image data. To begin with we sparse code the data using a learned basis dictionary and an index of the dictionary's support set is next used to generate one compact identifier for each data point. As basis combinations increase exponentially with an increasing support set, each data point is likely to get a unique identifier that can be used to index a hash table for fast NN operations. When dealing with real world data, the identifiers corresponding to the query point and the true nearest neighbors in the database seldom match exactly (due to image noise, distortion, etc.). To accommodate these near matches, we propose a novel extension of the framework that utilizes the regularization path of the LASSO formulation to create robust hash codes. Experiments are conducted on large datasets and demonstrate that our algorithm rivals state-of-the-art NN techniques in search time, accuracy and memory usage.