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One-shot learning of sketch categories with co-regularized sparse coding

Qi, Y., Zheng, W.-S., Xiang, T., Song, Yi-Zhe, Zhang, H. and Guo, J. (2014) One-shot learning of sketch categories with co-regularized sparse coding In: 10th International Symposium on Visual Computing (ISVC 2014), 08-10 Dec 2014, Las Vegas, NV, USA.

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Categorizing free-hand human sketches has profound implications in applications such as human computer interaction and image retrieval. The task is non-trivial due to the iconic nature of sketches, signified by large variances in both appearance and structure when compared with photographs. Prior works often utilize off-the-shelf low-level features and assume the availability of a large training set, rendering them sensitive towards abstraction and less scalable to new categories. To overcome this limitation, we propose a transfer learning framework which enables one-shot learning of sketch categories. The framework is based on a novel co-regularized sparse coding model which exploits common/ shareable parts among human sketches of seen categories and transfer them to unseen categories. We contribute a new dataset consisting of 7,760 human segmented sketches from 97 object categories. Extensive experiments reveal that the proposed method can classify unseen sketch categories given just one training sample with a 33.04% accuracy, offering a two-fold improvement over baselines.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Qi, Y.
Zheng, W.-S.
Xiang, T.
Zhang, H.
Guo, J.
Date : 2014
DOI : 10.1007/978-3-319-14364-4_8
Depositing User : Clive Harris
Date Deposited : 12 Aug 2019 13:21
Last Modified : 12 Aug 2019 13:21

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