• About
  • Policies
  • What is openaccess
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Industrial Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Industrial Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Semantic scene classification for image annotation and retrieval

      Thumbnail
      View / Download
      2.1 Mb
      Author
      Çavuş, Özge
      Aksoy, Selim
      Date
      2008-12
      Source Title
      Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
      Publisher
      Springer
      Pages
      402 - 410
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
      146
      views
      102
      downloads
      Abstract
      We describe an annotation and retrieval framework that uses a semantic image representation by contextual modeling of images using occurrence probabilities of concepts and objects. First, images are segmented into regions using clustering of color features and line structures. Next, each image is modeled using the histogram of the types of its regions, and Bayesian classifiers are used to obtain the occurrence probabilities of concepts and objects using these histograms. Given the observation that a single class with the highest probability is not sufficient to model image content in an unconstrained data set with a large number of semantically overlapping classes, we use the concept/object probabilities as a new representation, and perform retrieval in the semantic space for further improvement of the categorization accuracy. Experiments on the TRECVID and Corel data sets show good performance. © 2008 Springer Berlin Heidelberg.
      Keywords
      Image analysis
      Image enhancement
      Information theory
      Pattern recognition
      Probability
      Random processes
      Semantics
      Surface plasmon resonance
      Syntactics
      Technical presentations
      Bayesian classifiers
      Color features
      Contextual modeling
      Data sets
      Image annotations
      Line structures
      Model images
      Occurrence probabilities
      Retrieval frameworks
      Semantic images
      Semantic scene classifications
      Semantic spaces
      Trecvid
      Object recognition
      Line segment
      Probabilistic latent semantic analysis
      Permalink
      http://hdl.handle.net/11693/26791
      Published Version (Please cite this version)
      http://dx.doi.org/10.1007/978-3-540-89689-0_44
      Collections
      • Department of Computer Engineering 1368
      • Department of Industrial Engineering 677
      Show full item record

      Related items

      Showing items related by title, author, creator and subject.

      • Thumbnail

        Identification of individuals' emotional response to the indoor soundscape in public study areas via semantic differentiation 

        Acun, Volkan; Yilmazer, Semiha (Institute of Noise Control Engineering, 2017)
        Aims of this research is to identify individuals' emotional response to the sound environment of public study areas. The research has taken place in the public study areas of Bilkent University Campus. These study areas ...
      • Thumbnail

        Generic windowing support for extensible stream processing systems 

        Gedik, B. (John Wiley & Sons Ltd., 2014)
        Stream processing applications process high volume, continuous feeds from live data sources, employ data-in-motion analytics to analyze these feeds, and produce near real-time insights with low latency. One of the fundamental ...
      • Thumbnail

        Semantic argument frequency-based multi-document summarization 

        Aksoy, Cem; Buğdaycı, Ahmet; Gür, Tunay; Uysal, İbrahim; Can, Fazlı (IEEE, 2009-09)
        Semantic Role Labeling (SRL) aims to identify the constituents of a sentence, together with their roles with respect to the sentence predicates. In this paper, we introduce and assess the idea of using SRL on generic ...

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      Copyright © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy