Journal article Open Access

Techniques of Indoor-Outdoor Scene Classification using the VGG-16 CNN Model

Kajal Gupta; RK Sharma


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  <identifier identifierType="URL">https://zenodo.org/record/5832171</identifier>
  <creators>
    <creator>
      <creatorName>Kajal Gupta</creatorName>
      <affiliation>M.Tech degree, Department of Computer Science and  Engineering, Faculty Of Engineering and Technology, Agra College, Agra  (U.P.), India.</affiliation>
    </creator>
    <creator>
      <creatorName>RK Sharma</creatorName>
      <affiliation>Associate Professor, Faculty Of Engineering and  Technology, Agra College, Agra (U.P.), India.</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Techniques of Indoor-Outdoor Scene Classification  using the VGG-16 CNN Model</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Scene Classification, Indoor-Outdoor Classification, Deep Learning, Neural Network Model VGG 16, CCN, Data Augmentation, Imagedatagenerator, Optimizers.</subject>
    <subject subjectScheme="issn">2277-3878</subject>
    <subject subjectScheme="handle">100.1/ijrte.B62970710221</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)</contributorName>
      <affiliation>Publisher</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2021-07-30</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5832171</alternateIdentifier>
  </alternateIdentifiers>
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    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2277-3878</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijrte.B6297.0710221</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In the world of today, computers have begun to rule the people as the machines carry out practically every work that people can accomplish. Scene classification is one such concept that becomes increasingly important when robots replicate the actions of a human being Scene categorization may be done on interior or exterior scenes using various extraction techniques, as well as categorization of indoor and outdoor scenes in these two categories is more difficult. The methodology for the indoor/outdoor classification scene has the drawback of inadequate accuracy. This research aims to enhance the accuracy by using the Convolution Neural Network Model in VGG-16. This paper proposes a new approach to VGG-16 to classify images into their classes. The algorithm results are tested using SUN397- indoor-outdoor dataset &amp;amp; the tentative data reveal that the methodology proposed is superior to the existing technology for the scene classification of indoor-outdoor (I/U).&lt;/p&gt;</description>
  </descriptions>
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