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Supervised Feature Quantization with Entropy Optimization

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Author

Summary, in English

Feature quantization is a crucial component for efficient large scale image retrieval and object recognition. By quantizing local features into visual words, one hopes that features that match each other obtain the same word ID. Then, similarities between images can be measured with respect to the corresponding histograms of visual words. Given the appearance variations of local features, traditional quantization methods do not take into account the distribution of matched features. In this paper, we investigate how to encode additional prior information on the feature distribution via entropy optimization by leveraging ground truth correspondence data. We propose a computationally efficient optimization scheme for large scale vocabulary training. The results from our experiments suggest that entropy-optimized vocabulary performs better than unsupervised quantization methods in terms of recall and precision for feature matching. We also demonstrate the advantage of the optimized vocabulary for image retrieval.

Publishing year

2011

Language

English

Pages

1386-1393

Publication/Series

Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Computer Vision and Robotics (Autonomous Systems)
  • Mathematics

Keywords

  • visual vocabulary
  • entropy optimization

Conference name

1st IEEE Workshop on Information Theory in Computer Vision and Pattern Recognition (ICCV 2011), 2011

Conference date

2011-11-06 - 2011-11-13

Conference place

Barcelona, Spain

Status

Published

Research group

  • Mathematical Imaging Group

ISBN/ISSN/Other

  • ISBN: 978-1-4673-0062-9 (print)