Learning vocabulary-based hashing with adaboost

Authors:
Yingyu Liang;Jianmin Li;Bo Zhang
Affiliations:
State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...;State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...;State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...
Venue:
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Year:
2010

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Abstract

Approximate near neighbor search plays a critical role in various kinds of multimedia applications. The vocabulary-based hashing scheme uses vocabularies, i.e. selected sets of feature points, to define a hash function family. The function family can be employed to build an approximate near neighbor search index. The critical problem in vocabulary-based hashing is the criteria of choosing vocabularies. This paper proposes a approach to greedily choosing vocabularies via Adaboost. An index quality criterion is designed for the AdaBoost approach to adjust the weight of the training data. We also describe the parallelized version of the index for large scale applications. The promising results of the near-duplicate image detection experiments show the efficiency of the new vocabulary construction algorithm and desired qualities of the parallelized vocabulary-based hashing for large scale applications.