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

      Scene classification using bag-of-regions representations

      Thumbnail
      View / Download
      1.6 Mb
      Author
      Gökalp, Demir
      Aksoy, Selim
      Date
      2007-06
      Source Title
      Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
      Publisher
      IEEE
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
      188
      views
      148
      downloads
      Abstract
      This paper describes our work on classification of outdoor scenes. First, images are partitioned into regions using one-class classification and patch-based clustering algorithms where one-class classifiers model the regions with relatively uniform color and texture properties, and clustering of patches aims to detect structures in the remaining regions. Next, the resulting regions are clustered to obtain a codebook of region types, and two models are constructed for scene representation: a "bag of individual regions" representation where each region is regarded separately, and a "bag of region pairs" representation where regions with particular spatial relationships are considered, together. Given these representations, scene classification is done using Bayesian classifiers. We also propose a novel region selection algorithm that identifies region types that are frequently found in a particular class of scenes but rarely exist in other classes, and also consistently occur together in the same class of scenes. Experiments on the LabelMe data set showed that the proposed models significantly out-perform a baseline global feature-based approach. © 2007 IEEE.
      Keywords
      Bayesian networks
      Clustering algorithms
      Mathematical models
      Codebooks
      Global features
      Outdoor scenes
      Spatial relationships
      Image classification
      Permalink
      http://hdl.handle.net/11693/27090
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/CVPR.2007.383375
      Collections
      • Department of Computer Engineering 1398
      Show full item record

      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