Robust sparse hashing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

We study Nearest Neighbors (NN) retrieval by introducing a new approach: Robust Sparse Hashing (RSH). Our approach is inspired by the success of dictionary learning for sparse coding; the key innovation is to use learned sparse codes as hashcodes for speeding up NN. But sparse coding suffers from a major drawback: when data are noisy or uncertain, for a query point, an exact match of the hashcode seldom happens, breaking the NN retrieval. We tackle this difficulty via our novel dictionary learning and sparse coding framework called RSH by learning dictionaries on the robustified counterparts of uncertain data points. The algorithm is applied to NN retrieval for Scale Invariant Feature Transform (SIFT) descriptors. The results demonstrate that RSH is noise tolerant, and at the same time shows promising NN performance over the state-of-the-art.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Pages2417-2420
Number of pages4
DOIs
StatePublished - Dec 1 2012
Event2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL, United States
Duration: Sep 30 2012Oct 3 2012

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2012 19th IEEE International Conference on Image Processing, ICIP 2012
CountryUnited States
CityLake Buena Vista, FL
Period9/30/1210/3/12

Keywords

  • Nearest neighbors
  • Robust optimization
  • Sparse coding

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