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SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval

Xu, P., Huang, Y., Yuan, T., Pang, K., Song, Yi-Zhe, Xiang, T., Hospedales, T.M., Ma, Z. and Guo, J. (2018) SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18-23 Jun 2018, Salt Lake City, Utah, USA.

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We propose a deep hashing framework for sketch retrieval that, for the first time, works on a multi-million scale human sketch dataset. Leveraging on this large dataset, we explore a few sketch-specific traits that were otherwise under-studied in prior literature. Instead of following the conventional sketch recognition task, we introduce the novel problem of sketch hashing retrieval which is not only more challenging, but also offers a better testbed for large-scale sketch analysis, since: (i) more fine-grained sketch feature learning is required to accommodate the large variations in style and Abstraction, and (ii) a compact binary code needs to be learned at the same time to enable efficient retrieval. Key to our network design is the embedding of unique characteristics of human sketch, where (i) a two-branch CNN-RNN architecture is adapted to explore the temporal ordering of strokes, and (ii) a novel hashing loss is specifically designed to accommodate both the temporal and Abstract traits of sketches. By working with a 3.8M sketch dataset, we show that state-of-the-art hashing models specifically engineered for static images fail to perform well on temporal sketch data. Our network on the other hand not only offers the best retrieval performance on various code sizes, but also yields the best generalization performance under a zero-shot setting and when re-purposed for sketch recognition. Such superior performances effectively demonstrate the benefit of our sketch-specific design. © 2018 IEEE.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Xu, P.
Huang, Y.
Yuan, T.
Pang, K.
Xiang, T.
Hospedales, T.M.
Ma, Z.
Guo, J.
Date : 2018
DOI : 10.1109/CVPR.2018.00844
Uncontrolled Keywords : Task analysis; Visualization; Semantics; Binary codes; Quantization (signal); Entropy; Computer architecture
Related URLs :
Additional Information : Printed proceedings published by Curran Associates Inc., ISBN 9781538664216
Depositing User : Clive Harris
Date Deposited : 02 Jul 2019 11:02
Last Modified : 02 Jul 2019 11:02

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