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Authors: Sean Ryan Fanello 1 ; Nicoletta Noceti 2 ; Giorgio Metta 3 and Francesca Odone 2

Affiliations: 1 Istituto Italiano di Tecnologia and Università degli Studi di Genova, Italy ; 2 Università degli Studi di Genova, Italy ; 3 Istituto Italiano di Tecnologia, Italy

Keyword(s): Dictionary based Image Pooling, Sparse Representation, Object Categorization, iCub, iCubWorld Data-Set.

Abstract: It is well known that image representations learned through ad-hoc dictionaries improve the overall results in object categorization problems. Following the widely accepted coding-pooling visual recognition pipeline, these representations are often tightly coupled with a coding stage. In this paper we show how to exploit ad-hoc representations both within the coding and the pooling phases. We learn a dictionary for each object class and then use local descriptors encoded with the learned atoms to guide the pooling operator. We exhaustively evaluate the proposed approach in both single instance object recognition and object categorization problems. From the applications standpoint we consider a classical image retrieval scenario with the Caltech 101, as well as a typical robot vision task with data acquired by the iCub humanoid robot.

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Paper citation in several formats:
Fanello, S.; Noceti, N.; Metta, G. and Odone, F. (2014). Dictionary based Pooling for Object Categorization. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP; ISBN 978-989-758-004-8; ISSN 2184-4321, SciTePress, pages 269-274. DOI: 10.5220/0004654602690274

@conference{visapp14,
author={Sean Ryan Fanello. and Nicoletta Noceti. and Giorgio Metta. and Francesca Odone.},
title={Dictionary based Pooling for Object Categorization},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP},
year={2014},
pages={269-274},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004654602690274},
isbn={978-989-758-004-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP
TI - Dictionary based Pooling for Object Categorization
SN - 978-989-758-004-8
IS - 2184-4321
AU - Fanello, S.
AU - Noceti, N.
AU - Metta, G.
AU - Odone, F.
PY - 2014
SP - 269
EP - 274
DO - 10.5220/0004654602690274
PB - SciTePress

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