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Image Set Classification via Template Triplets and Context-Aware Similarity Embedding

  • Feng-Ju Chang
  • Ram Nevatia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10115)

Abstract

We present a template-triplet-based embedding approach to optimize the ensemble SoftMax similarity between templates (sets) for improved image set classification. More specifically, a triplet is created among “three” whole templates or subtemplates of images to incorporate the (sub)template structure into metric learning. To further account for intra-class variations of images, we introduce a factorization technique to integrate image-specific context for learning sample-specific embedding. We evaluate our approach on several benchmark datasets, and demonstrate its effectiveness for image set classification.

Keywords

Face Recognition Local Binary Pattern Equal Error Rate Stochastic Gradient Descent False Acceptance Rate 
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

Acknowledgement

This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA 2014-14071600010. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon. Moreover, we gratefully acknowledge USC HPC for hyper-computing.

Supplementary material

440742_1_En_15_MOESM1_ESM.pdf (192 kb)
Supplementary material 1 (pdf 191 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute for Robotics and Intelligent SystemsUniveristy of Southern CaliforniaLos AngelesUSA

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