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Deep Embedding Using Bayesian Risk Minimization with Application to Sketch Recognition

Mishra, A and Singh, AK (2019) Deep Embedding Using Bayesian Risk Minimization with Application to Sketch Recognition. In: 14th Asian Conference on Computer Vision, ACCV 2018, 2 December 2018 - 6 December 2018, Perth, pp. 357-370.

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Official URL: https://doi.org/10.1007/978-3-030-20873-8_23


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.

Item Type: Conference Paper
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Verlag
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Computer vision; Decision theory; Deep neural networks; Embeddings; Risk perception, Bayesian decision theory; Bayesian risks; Hand-drawn sketches; Intra-class variation; Metric learning; Misclassifications; Sketch recognition; State of the art, Drawing (graphics)
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 27 Oct 2022 08:13
Last Modified: 27 Oct 2022 08:13
URI: https://eprints.iisc.ac.in/id/eprint/77601

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