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Supervised Feature Quantization with Entropy Optimization

Kuang, Yubin LU ; Byröd, Martin LU and Åström, Karl LU (2011) 1st IEEE Workshop on Information Theory in Computer Vision and Pattern Recognition (ICCV 2011), 2011 In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on p.1386-1393
Abstract (Swedish)
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
visual vocabulary, entropy optimization
in
Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on
pages
8 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
1st IEEE Workshop on Information Theory in Computer Vision and Pattern Recognition (ICCV 2011), 2011
external identifiers
  • WOS:000300056700199
  • Scopus:84856683513
ISBN
978-1-4673-0062-9 (print)
DOI
10.1109/ICCVW.2011.6130413
language
English
LU publication?
yes
id
9f1f70f9-27fd-40fe-9da6-8520edf4434d (old id 2214469)
date added to LUP
2012-02-07 17:54:57
date last changed
2016-10-13 04:47:22
@misc{9f1f70f9-27fd-40fe-9da6-8520edf4434d,
  abstract     = {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.},
  author       = {Kuang, Yubin and Byröd, Martin and Åström, Karl},
  isbn         = {978-1-4673-0062-9 (print)},
  keyword      = {visual vocabulary,entropy optimization},
  language     = {eng},
  pages        = {1386--1393},
  publisher    = {ARRAY(0xb8eb970)},
  series       = {Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on},
  title        = {Supervised Feature Quantization with Entropy Optimization},
  url          = {http://dx.doi.org/10.1109/ICCVW.2011.6130413},
  year         = {2011},
}