Efficient Nearest Neighbors via Robust Sparse Hashing

Anoop Cherian, Suvrit Sra, Vassilios Morellas, Nikolaos Papanikolopoulos
2014 IEEE Transactions on Image Processing  
This work presents a new Nearest Neighbor (NN) retrieval framework: Robust Sparse Hashing (RSH). Our approach is inspired by the success of dictionary learning for sparse coding. Our key idea is to sparse code the data using a learned dictionary, and then to generate hash codes out of these sparse codes for accurate and fast NN retrieval. But direct application of sparse coding to NN retrieval poses a technical difficulty: when data are noisy or uncertain (which is the case with most real world
more » ... datasets), for a query point, an exact match of the hash code generated from the sparse code seldom happens, thereby breaking the NN retrieval. Borrowing ideas from robust optimization theory, we circumvent this difficulty via our novel robust dictionary learning and sparse coding framework called RSH, by learning dictionaries on the robustified counterparts of the perturbed data points. The algorithm is applied to NN retrieval on both simulated and real world data. Our results demonstrate that RSH holds significant promise for efficient NN retrieval against the state of the art. and SECTTRA. His research interests include robotics, computer vision, sensors for transportation applications, and control. His transportation research has included projects involving vision-based sensing and classification of vehicles, and the human activity recognition.
doi:10.1109/tip.2014.2324280 pmid:25122742 fatcat:rcopo4azqfeqdhpqjx2svdq55m