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Large Scale Sparse Optimization for Object Detection in High Resolution Images

Identifieur interne : 000227 ( France/Analysis ); précédent : 000226; suivant : 000228

Large Scale Sparse Optimization for Object Detection in High Resolution Images

Auteurs : Aurélie Boisbunon [France] ; Rémi Flamary [France] ; Alain Rakotomamonjy [France] ; Alain Giros [France] ; Josiane Zerubia [France]

Source :

RBID : Hal:hal-01066235

English descriptors

Abstract

In this work, we address the problem of detecting objects in images by expressing the image as convolutions between activation matrices and dictionary atoms. The activation matrices are estimated through sparse optimization and correspond to the position of the objects. In particular, we propose an efficient algorithm based on an active set strategy that is easily scalable and can be computed in parallel. We apply it to a toy image and a satellite image where the aim is to detect all the boats in a harbor. These results show the benefit of using nonconvex penalties, such as the log-sum penalty, over the convex l1 penalty.

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Hal:hal-01066235

Le document en format XML

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<tutelle active="#struct-300009" type="indirect">
<org type="institution" xml:id="struct-300009" status="VALID">
<orgName>Institut National de Recherche en Informatique et en Automatique</orgName>
<orgName type="acronym">Inria</orgName>
<desc>
<address>
<addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.inria.fr/en/</ref>
</desc>
</org>
</tutelle>
</tutelles>
</hal:affiliation>
<country>France</country>
</affiliation>
</author>
</analytic>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc>
<textClass>
<keywords scheme="mix" xml:lang="en">
<term>active set</term>
<term>image processing</term>
<term>object detection</term>
<term>sparsity</term>
<term>unsupervised learning</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">In this work, we address the problem of detecting objects in images by expressing the image as convolutions between activation matrices and dictionary atoms. The activation matrices are estimated through sparse optimization and correspond to the position of the objects. In particular, we propose an efficient algorithm based on an active set strategy that is easily scalable and can be computed in parallel. We apply it to a toy image and a satellite image where the aim is to detect all the boats in a harbor. These results show the benefit of using nonconvex penalties, such as the log-sum penalty, over the convex l1 penalty.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>France</li>
</country>
<region>
<li>Haute-Normandie</li>
<li>Provence-Alpes-Côte d'Azur</li>
<li>Région Normandie</li>
</region>
<settlement>
<li>Le Havre</li>
<li>Nice</li>
<li>Rouen</li>
</settlement>
<orgName>
<li>Université Nice Sophia Antipolis</li>
<li>Université de Rouen</li>
<li>Université du Havre</li>
</orgName>
</list>
<tree>
<country name="France">
<noRegion>
<name sortKey="Boisbunon, Aurelie" sort="Boisbunon, Aurelie" uniqKey="Boisbunon A" first="Aurélie" last="Boisbunon">Aurélie Boisbunon</name>
</noRegion>
<name sortKey="Flamary, Remi" sort="Flamary, Remi" uniqKey="Flamary R" first="Rémi" last="Flamary">Rémi Flamary</name>
<name sortKey="Giros, Alain" sort="Giros, Alain" uniqKey="Giros A" first="Alain" last="Giros">Alain Giros</name>
<name sortKey="Rakotomamonjy, Alain" sort="Rakotomamonjy, Alain" uniqKey="Rakotomamonjy A" first="Alain" last="Rakotomamonjy">Alain Rakotomamonjy</name>
<name sortKey="Zerubia, Josiane" sort="Zerubia, Josiane" uniqKey="Zerubia J" first="Josiane" last="Zerubia">Josiane Zerubia</name>
</country>
</tree>
</affiliations>
</record>

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