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Instance-level coupled subspace learning for fine-grained sketch-based image retrieval

Xu, P., Yin, Q., Qi, Y., Song, Yi-Zhe, Ma, Z., Wang, L. and Guo, J. (2016) Instance-level coupled subspace learning for fine-grained sketch-based image retrieval In: Computer Vision ECCV 2016 Workshops (ECCV 2016), 08-10 and 15-16 Oct 2016, Amsterdam, The Netherlands.

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Abstract

Fine-grained sketch-based image retrieval (FG-SBIR) is a newly emerged topic in computer vision. The problem is challenging because in addition to bridging the sketch-photo domain gap, it also asks for instance-level discrimination within object categories. Most prior approaches focused on feature engineering and fine-grained ranking, yet neglected an important and central problem: how to establish a finegrained cross-domain feature space to conduct retrieval. In this paper, for the first time we formulate a cross-domain framework specifically designed for the task of FG-SBIR that simultaneously conducts instancelevel retrieval and attribute prediction. Different to conventional phototext cross-domain frameworks that performs transfer on category-level data, our joint multi-view space uniquely learns from the instance-level pair-wise annotations of sketch and photo. More specifically, we propose a joint view selection and attribute subspace learning algorithm to learn domain projection matrices for photo and sketch, respectively. It follows that visual attributes can be extracted from such matrices through projection to build a coupled semantic space to conduct retrieval. Experimental results on two recently released fine-grained photo-sketch datasets show that the proposed method is able to perform at a level close to those of deep models, while removing the need for extensive manual annotations.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Xu, P.
Yin, Q.
Qi, Y.
Song, Yi-Zhey.song@surrey.ac.uk
Ma, Z.
Wang, L.
Guo, J.
Date : 18 September 2016
DOI : 10.1007/978-3-319-46604-0_2
Uncontrolled Keywords : Attribute prediction; Attribute supervision; Fine-grained SBIR; Multi-view domain adaptation
Depositing User : Clive Harris
Date Deposited : 12 Aug 2019 13:00
Last Modified : 12 Aug 2019 13:00
URI: http://epubs.surrey.ac.uk/id/eprint/852128

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