• 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.

      Nearest - neighbor based metric functions for indoor scene recognition

      Thumbnail
      View/Open
      Full printable version (2.306Mb)
      Author
      Cakir, F.
      Güdükbay, U.
      Ulusoy, Ö.
      Date
      2011
      Journal Title
      Computer Vision and Image Understanding
      ISSN
      1077-3142
      Publisher
      Academic Press
      Volume
      115
      Issue
      11
      Pages
      1483 - 1492
      Language
      English
      Type
      Article
      Metadata
      Show full item record
      Please cite this item using this persistent URL
      http://hdl.handle.net/11693/21729
      Abstract
      Indoor scene recognition is a challenging problem in the classical scene recognition domain due to the severe intra-class variations and inter-class similarities of man-made indoor structures. State-of-the-art scene recognition techniques such as capturing holistic representations of an image demonstrate low performance on indoor scenes. Other methods that introduce intermediate steps such as identifying objects and associating them with scenes have the handicap of successfully localizing and recognizing the objects in a highly cluttered and sophisticated environment. We propose a classification method that can handle such difficulties of the problem domain by employing a metric function based on the Nearest-Neighbor classification procedure using the bag-of-visual words scheme, the so-called codebooks. Considering the codebook construction as a Voronoi tessellation of the feature space, we have observed that, given an image, a learned weighted distance of the extracted feature vectors to the center of the Voronoi cells gives a strong indication of the image's category. Our method outperforms state-of-the-art approaches on an indoor scene recognition benchmark and achieves competitive results on a general scene dataset, using a single type of descriptor. © 2011 Elsevier Inc. All rights reserved.
      Published as
      http://dx.doi.org/10.1016/j.cviu.2011.07.007
      Collections
      • Department of Computer Engineering 1107

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage Statistics

      Bilkent University

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

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