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Deep Embedding using Bayesian Risk Minimization with Application to Sketch Recognition

2018-12-06 11:20:58
Anand Mishra, Ajeet Kumar Singh
arXiv_CV
         
Abstract

In this paper, we address the problem of hand-drawn sketch recognition. Inspired by the Bayesian decision theory, we present a deep metric learning loss with the objective to minimize the Bayesian risk of misclassification. We estimate this risk for every mini-batch during training, and learn robust deep embeddings by backpropagating it to a deep neural network in an end-to-end trainable paradigm. Our learnt embeddings are discriminative and robust despite of intra-class variations and inter-class similarities naturally present in hand-drawn sketch images. Outperforming the state of the art on sketch recognition, our method achieves 82.2% and 88.7% on TU-Berlin-250 and TU-Berlin-160 benchmarks respectively.

Abstract (translated)

URL

https://arxiv.org/abs/1812.02466

PDF

https://arxiv.org/pdf/1812.02466.pdf

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