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Sketching with Style: Visual Search with Sketches and Aesthetic Context

Collomosse, John, Bui, Tu, Wilber, M, Fang, C and Jin, H (2017) Sketching with Style: Visual Search with Sketches and Aesthetic Context In: 2017 IEEE International Conference on Computer Vision (ICCV), 22-29 Oct. 2017, Venice, Italy.

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We propose a novel measure of visual similarity for image retrieval that incorporates both structural and aesthetic (style) constraints. Our algorithm accepts a query as sketched shape, and a set of one or more contextual images specifying the desired visual aesthetic. A triplet network is used to learn a feature embedding capable of measuring style similarity independent of structure, delivering significant gains over previous networks for style discrimination. We incorporate this model within a hierarchical triplet network to unify and learn a joint space from two discriminatively trained streams for style and structure. We demonstrate that this space enables, for the first time, style-constrained sketch search over a diverse domain of digital artwork comprising graphics, paintings and drawings. We also briefly explore alternative query modalities.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Wilber, M
Fang, C
Jin, H
Date : 25 December 2017
DOI : 10.1109/ICCV.2017.290
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Depositing User : Melanie Hughes
Date Deposited : 18 Sep 2018 14:01
Last Modified : 27 Nov 2018 15:41

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