Robust object recognition via weakly supervised metric and template learning

Min Tan

doi:10.1016/j.neucom.2015.04.123

Robust object recognition via weakly supervised metric and template learning

2016-03-01

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DOI：10.1016/j.neucom.2015.04.123

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In this paper, we present a new framework for object recognition via weakly supervised metric and template learning, wherein the optimal metric and templates are jointly learned. Its advantages include high computational speed, and robustness against image noise and unbalanced training data. Specifically, considering the noise in the training data, our framework is formulated as a weakly supervised learning model in which images with higher reliability will contribute more to the training result. A latent structural SVM based Weakly Supervised Metric and Template Learning (WSMTL) method is designed to jointly learn the metric, the templates, and a weight vector. The weight vector is used to represent each image's reliability. With the learned metric and object templates, each testing sample is recognized via 1-NN searching within templates. Owing to the 1-NN searching scheme in the recognition phase, WSMTL is of great computational efficiency. We used CMU PIE database with synthesized noise to evaluate the robustness of WSMTL. Experimental results show that WSMTL is robust against noise and unbalanced training data. Moreover, we compared it with some state-of-the-art recognition methods on the public traffic sign dataset BTSC and human face database, i.e., Extended Yale-B. The comparison results demonstrate that our method outperforms the others in object recognition tasks. (C) 2015 Elsevier B.V. All rights reserved.

Keywords:

Metric learning

Template learning

Object recognition

Weakly supervised learning

Robust object recognition via weakly supervised metric and template learning

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