# Deep Embedding using Bayesian Risk Minimization with Application to Sketch Recognition

## TL;DR

This paper introduces a deep metric learning approach based on Bayesian risk minimization for hand-drawn sketch recognition, achieving state-of-the-art accuracy by learning robust, discriminative embeddings.

## Contribution

It proposes a novel Bayesian risk-based loss function for deep metric learning, specifically tailored for sketch recognition, and demonstrates its effectiveness on benchmark datasets.

## Key findings

- Achieves 82.2% accuracy on TU-Berlin-250
- Achieves 88.7% accuracy on TU-Berlin-160
- Outperforms existing methods in sketch recognition

## 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.