Adaptive quantization for hashing: An information-based approach to learning binary codes

Caiming Xiong, Wei Chen, Gang Chen, David Johnson, Jason J. Corso

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Large-scale data mining and retrieval applications have increasingly turned to compact binary data representations as a way to achieve both fast queries and efficient data storage: many algorithms have been proposed for learning effective binary encodings. Most of these algorithms focus on learning a set of projection hyperplanes for the data and simply binarizing the result from each hyperplane, but this neglects the fact that informativeness may not be uniformly distributed across the projections. In this paper, we address this issue by proposing a novel adaptive quantization (AQ) strategy that adaptively assigns varying numbers of bits to different hyperplanes based on their information content. Our method provides an information-based schema that preserves the neighborhood structure of data points, and we jointly find the globally optimal bit-allocation for all hyperplanes. In our experiments, we compare with state-of-the-art methods on four large-scale datasets and find that our adaptive quantization approach significantly improves on traditional hashing methods.

Original languageEnglish
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsMohammed J. Zaki, Arindam Banerjee, Srinivasan Parthasarathy, Pang Ning-Tan, Zoran Obradovic, Chandrika Kamath
Pages172-180
Number of pages9
ISBN (Electronic)9781510811515
DOIs
StatePublished - 2014
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: 24 Apr 201426 Apr 2014

Publication series

NameSIAM International Conference on Data Mining 2014, SDM 2014
Volume1

Conference

Conference14th SIAM International Conference on Data Mining, SDM 2014
Country/TerritoryUnited States
CityPhiladelphia
Period24/04/1426/04/14

Fingerprint

Dive into the research topics of 'Adaptive quantization for hashing: An information-based approach to learning binary codes'. Together they form a unique fingerprint.

Cite this