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SSP: Supervised Sparse Projections for Large-Scale Retrieval in High Dimensions

  • Frederick TungEmail author
  • James J. Little
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10111)

Abstract

As “big data” transforms the way we solve computer vision problems, the question of how we can efficiently leverage large labelled databases becomes increasingly important. High-dimensional features, such as the convolutional neural network activations that drive many leading recognition frameworks, pose particular challenges for efficient retrieval. We present a novel method for learning compact binary codes in which the conventional dense projection matrix is replaced with a discriminatively-trained sparse projection matrix. The proposed method achieves two to three times faster encoding than modern dense binary encoding methods, while obtaining comparable retrieval accuracy, on SUN RGB-D, AwA, and ImageNet datasets. The method is also more accurate than unsupervised high-dimensional binary encoding methods at similar encoding speeds.

Keywords

Binary Code Projection Matrix Convolutional Neural Network Retrieval Accuracy Scene Categorization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank Yan Xia for helpful discussion. This work was funded in part by the Natural Sciences and Engineering Research Council of Canada.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of British ColumbiaVancouverCanada

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