University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Deep Manifold Alignment for Mid-Grain Sketch Based Image Retrieval

Bui, Tu, Ribeiro, Leonardo, Ponti, Moacir and Collomosse, John (2019) Deep Manifold Alignment for Mid-Grain Sketch Based Image Retrieval In: 14th Asian Conference on Computer Vision, 02-06 Dec 2018, Perth, Australia.

[img] Text
Deep Manifold Alignment for Mid-Grain Sketch Based Image Retrieval.pdf - Accepted version Manuscript
Restricted to Repository staff only until 30 May 2020.

Download (4MB)

Abstract

We present an algorithm for visually searching image collections using free-hand sketched queries. Prior sketch based image retrieval (SBIR) algorithms adopt either a category-level or fine-grain (instance-level) definition of cross-domain similarity—returning images that match the sketched object class (category-level SBIR), or a specific instance of that object (fine-grain SBIR). In this paper we take the middle-ground; proposing an SBIR algorithm that returns images sharing both the object category and key visual characteristics of the sketched query without assuming photo-approximate sketches from the user. We describe a deeply learned cross-domain embedding in which ‘mid-grain’ sketch-image similarity may be measured, reporting on the efficacy of unsupervised and semi-supervised manifold alignment techniques to encourage better intra-category (mid-grain) discrimination within that embedding. We propose a new mid-grain sketch-image dataset (MidGrain65c) and demonstrate not only mid-grain discrimination, but also improved category-level discrimination using our approach.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Bui, Tut.v.bui@surrey.ac.uk
Ribeiro, Leonardo
Ponti, Moacir
Collomosse, JohnJ.Collomosse@surrey.ac.uk
Date : 29 May 2019
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1007/978-3-030-20893-6_20
Copyright Disclaimer : © Springer Nature Switzerland AG 2019
Uncontrolled Keywords : SBIR; Manifold alignment; Visual search
Related URLs :
Depositing User : Clive Harris
Date Deposited : 09 Sep 2019 11:13
Last Modified : 09 Sep 2019 12:34
URI: http://epubs.surrey.ac.uk/id/eprint/852572

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800