Supervised Feature Quantization with Entropy Optimization

Yubin Kuang, Martin Byröd, Karl Åström

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7 Citeringar (SciVal)


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.
Titel på gästpublikationComputer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on
FörlagIEEE - Institute of Electrical and Electronics Engineers Inc.
Antal sidor8
ISBN (tryckt)978-1-4673-0062-9 (print)
StatusPublished - 2011
Evenemang1st IEEE Workshop on Information Theory in Computer Vision and Pattern Recognition (ICCV 2011), 2011 - Barcelona, Spanien
Varaktighet: 2011 nov 62011 nov 13


Konferens1st IEEE Workshop on Information Theory in Computer Vision and Pattern Recognition (ICCV 2011), 2011

Ämnesklassifikation (UKÄ)

  • Datorseende och robotik (autonoma system)
  • Matematik


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