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Free-hand sketch recognition by multi-kernel feature learning

Li, Y., Hospedales, T.M., Song, Yi-Zhe and Gong, S. (2015) Free-hand sketch recognition by multi-kernel feature learning Computer Vision and Image Understanding, 137. pp. 1-11.

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Abstract

Abstract Free-hand sketch recognition has become increasingly popular due to the recent expansion of portable touchscreen devices. However, the problem is non-trivial due to the complexity of internal structures that leads to intra-class variations, coupled with the sparsity in visual cues that results in inter-class ambiguities. In order to address the structural complexity, a novel structured representation for sketches is proposed to capture the holistic structure of a sketch. Moreover, to overcome the visual cue sparsity problem and therefore achieve state-of-the-art recognition performance, we propose a Multiple Kernel Learning (MKL) framework for sketch recognition, fusing several features common to sketches. We evaluate the performance of all the proposed techniques on the most diverse sketch dataset to date (Mathias et al., 2012), and offer detailed and systematic analyses of the performance of different features and representations, including a breakdown by sketch-super-category. Finally, we investigate the use of attributes as a high-level feature for sketches and show how this complements low-level features for improving recognition performance under the MKL framework, and consequently explore novel applications such as attribute-based retrieval.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Li, Y.
Hospedales, T.M.
Song, Yi-Zhey.song@surrey.ac.uk
Gong, S.
Date : August 2015
DOI : 10.1016/j.cviu.2015.02.003
Uncontrolled Keywords : Attributes; Ensemble matching; Multiple kernel learning; Sketch recognition; Star graph
Depositing User : Clive Harris
Date Deposited : 23 Jul 2019 14:58
Last Modified : 23 Jul 2019 14:58
URI: http://epubs.surrey.ac.uk/id/eprint/852131

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