Conference paper Open Access

# Incorporation of Semantic Segmentation Information in Deep Hashing Techniques for Image Retrieval

Gkountakos Konstantinos; Semertzidis Theodoros; Papadopoulos Georgios Th.; Daras Petros

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<identifier identifierType="DOI">10.5281/zenodo.1076432</identifier>
<creators>
<creator>
<creatorName>Gkountakos Konstantinos</creatorName>
<affiliation>Centre for Research and Technology Hellas</affiliation>
</creator>
<creator>
<creatorName>Semertzidis Theodoros</creatorName>
<affiliation>Centre for Research and Technology Hellas</affiliation>
</creator>
<creator>
<affiliation>Centre for Research and Technology Hellas</affiliation>
</creator>
<creator>
<creatorName>Daras Petros</creatorName>
<affiliation>Centre for Research and Technology Hellas</affiliation>
</creator>
</creators>
<titles>
<title>Incorporation of Semantic Segmentation Information in Deep Hashing Techniques for Image Retrieval</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2017</publicationYear>
<subjects>
<subject>Hashing</subject>
<subject>Deep learning</subject>
<subject>Image retrieval</subject>
</subjects>
<dates>
<date dateType="Issued">2017-06-29</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1076432</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1076431</relatedIdentifier>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;Extracting discriminative image features for similarity search in nowadays large-scale databases becomes an imperative issue of paramount importance. To address the so called task of Approximate Nearest Neighbor (ANN) search in large visual dataset, deep hashing methods (i.e. approaches that make use of the recent deep learning paradigm in computer vision) have recently been introduced. In this paper, a novel approach to deep hashing is proposed, which incorporates local-level information, in the form of image semantic segmentation masks, during the hash code learning step. The proposed framework makes use of pixel-level classification labels, i.e. following a point-wise supervised learning methodology. Experimental evaluation in the significantly challenging domain of on-line terrorist propaganda video analysis, i.e. a highly diverse and heterogeneous application case, demonstrates the efficiency of the proposed approach.&lt;/p&gt;</description>
</descriptions>
<fundingReferences>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/700367/">700367</awardNumber>
<awardTitle>Detecting and ANalysing TErrorist-related online contents and financing activities</awardTitle>
</fundingReference>
</fundingReferences>
</resource>

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