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Output details

11 - Computer Science and Informatics

University of Bath

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Output title

Finding semantic structures in image hierarchies using Laplacian graph energy

Type

E - Conference contribution

DOI

10.1007/978-3-642-15561-1_50

Name of conference/published proceedings

Computer Vision, ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part IV

Volume number

6314

Issue number

First page of article

694

ISSN of proceedings

0302-9743

Year of publication

2010

URL

http://dx.doi.org/10.1007/978-3-642-15561-1_50

Number of additional authors

Additional information

<23>Hierarchical image segmentations are often overly complex. Our method reduces complexity by an order of magnitude without information loss. Published in ECCV, a highly ranked Computer Vision conference with an acceptance rate around 20%, this work provided a foundation for our later work with tracking (Neurocomputing 2012), object recognition (BMVC 2012,BMVC 2013), and non-photorealistic rendering (TVCG 2013,CAe 2013). The method is now in every day use in our EPSRC project ”Classifying Images Regardless of Depictive Style”. It was developed jointly with UC Berkeley, and is routinely used there too. It has also influenced research at Kyushu University (Perera’s PhD thesis).

Interdisciplinary

Cross-referral requested

Research group

C - Graphics & Vision

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract