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Universal sketch perceptual grouping

Li, K., Pang, K., Song, J., Song, Yi-Zhe, Xiang, T., Hospedales, T.M. and Zhang, H. (2018) Universal sketch perceptual grouping In: European Conference on Computer Vision (ECCV 2018), 08-14 Sep 2018, Munich, Germany.

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Abstract

In this work we aim to develop a universal sketch grouper. That is, a grouper that can be applied to sketches of any category in any domain to group constituent strokes/segments into semantically meaningful object parts. The first obstacle to this goal is the lack of large-scale datasets with grouping annotation. To overcome this, we contribute the largest sketch perceptual grouping (SPG) dataset to date, consisting of 20, 000 unique sketches evenly distributed over 25 object categories. Furthermore, we propose a novel deep universal perceptual grouping model. The model is learned with both generative and discriminative losses. The generative losses improve the generalisation ability of the model to unseen object categories and datasets. The discriminative losses include a local grouping loss and a novel global grouping loss to enforce global grouping consistency. We show that the proposed model significantly outperforms the state-of-the-art groupers. Further, we show that our grouper is useful for a number of sketch analysis tasks including sketch synthesis and fine-grained sketch-based image retrieval (FG-SBIR). © Springer Nature Switzerland AG 2018.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Li, K.
Pang, K.
Song, J.
Song, Yi-Zhey.song@surrey.ac.uk
Xiang, T.
Hospedales, T.M.
Zhang, H.
Date : 7 October 2018
DOI : 10.1007/978-3-030-01237-3_36
Uncontrolled Keywords : Dataset; Deep grouping model; Sketch perceptual grouping; Universal grouper
Depositing User : Clive Harris
Date Deposited : 30 Jul 2019 13:44
Last Modified : 30 Jul 2019 13:44
URI: http://epubs.surrey.ac.uk/id/eprint/852113

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