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A performance evaluation of gradient field HOG descriptor for sketch based image retrieval

Hu, R and Collomosse, J (2013) A performance evaluation of gradient field HOG descriptor for sketch based image retrieval Computer Vision and Image Understanding. pp. 790-806.

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Abstract

We present an image retrieval system for the interactive search of photo collections using free-hand sketches depicting shape. We describe Gradient Field HOG (GF-HOG); an adapted form of the HOG descriptor suitable for Sketch Based Image Retrieval (SBIR). We incorporate GF-HOG into a Bag of Visual Words (BoVW) retrieval framework, and demonstrate how this combination may be harnessed both for robust SBIR, and for localizing sketched objects within an image. We evaluate over a large Flickr sourced dataset comprising 33 shape categories, using queries from 10 non-expert sketchers. We compare GF-HOG against state-of-the-art descriptors with common distance measures and language models for image retrieval, and explore how affine deformation of the sketch impacts search performance. GF-HOG is shown to consistently outperform retrieval versus SIFT, multi-resolution HOG, Self Similarity, Shape Context and Structure Tensor. Further, we incorporate semantic keywords into our GF-HOG system to enable the use of annotated sketches for image search. A novel graph-based measure of semantic similarity is proposed and two applications explored: semantic sketch based image retrieval and a semantic photo montage.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
AuthorsEmailORCID
Hu, RUNSPECIFIEDUNSPECIFIED
Collomosse, JUNSPECIFIEDUNSPECIFIED
Date : 1 July 2013
Identification Number : 10.1016/j.cviu.2013.02.005
Additional Information : NOTICE: this is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Vision and Image Understanding, July 2013, DOI 10.1016/j.cviu.2013.02.005.
Depositing User : Symplectic Elements
Date Deposited : 03 Jun 2014 08:27
Last Modified : 09 Jun 2014 13:57
URI: http://epubs.surrey.ac.uk/id/eprint/805251

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