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Cross-modal subspace learning for sketch-based image retrieval: A comparative study

Xu, P., Li, K., Ma, Z., Song, Yi-Zhe, Wang, L. and Guo, J. (2017) Cross-modal subspace learning for sketch-based image retrieval: A comparative study In: 5th IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC 2016), 23-25 Sep 2016, 5th IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC 2016).

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Abstract

Sketch-based image retrieval (SBIR) has become a prominent research topic in recent years due to the proliferation of touch screens. The problem is however very challenging for that photos and sketches are inherently modeled in different modalities. Photos are accurate (colored and textured) depictions of the real-world, whereas sketches are highly abstract (black and white) renderings often drawn from human memory. This naturally motivates us to study the effectiveness of various cross-modal retrieval methods in SBIR. However, to the best of our knowledge, all established cross-modal algorithms are designed to traverse the more conventional cross-modal gap of image and text, making their general applicableness to SBIR unclear. In this paper, we design a series of experiments to clearly illustrate circumstances under which cross-modal methods can be best utilized to solve the SBIR problem. More specifically, we choose six state-of-the-art cross-modal subspace learning approaches that were shown to work well on image-text and conduct extensive experiments on a recently released SBIR dataset. Finally, we present detailed comparative analysis of the experimental results and offer insights to benefit future research.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Xu, P.
Li, K.
Ma, Z.
Song, Yi-Zhey.song@surrey.ac.uk
Wang, L.
Guo, J.
Date : August 2017
DOI : 10.1109/ICNIDC.2016.7974625
Uncontrolled Keywords : Comparison; Cross-modal; SBIR; Subspace learning
Related URLs :
Additional Information : Printed proceedings published by Curran Associates Inc. ISBN: 978-1-5090-1247-3
Depositing User : Clive Harris
Date Deposited : 08 Jul 2019 15:05
Last Modified : 08 Jul 2019 15:05
URI: http://epubs.surrey.ac.uk/id/eprint/852119

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