August 2010
Volume 10, Issue 7
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2010
Object recognition based on hierarchically organized structures of natural objects
Author Affiliations
  • Xiaofu He
    Brain and Behavior Discovery Institute, Medical College of Georgia, Augusta, GA
  • Joe Tsien
    Brain and Behavior Discovery Institute, Medical College of Georgia, Augusta, GA
    Department of Neurology, Medical College of Georgia, Augusta, GA
  • Zhiyong Yang
    Brain and Behavior Discovery Institute, Medical College of Georgia, Augusta, GA
    Department of Ophthalmology, Medical College of Georgia, Augusta, GA
Journal of Vision August 2010, Vol.10, 989. doi:10.1167/10.7.989
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Xiaofu He, Joe Tsien, Zhiyong Yang; Object recognition based on hierarchically organized structures of natural objects. Journal of Vision 2010;10(7):989. doi: 10.1167/10.7.989.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Humans can recognize objects quickly and accurately despite tremendous variations in their appearance. This amazing ability of rapid categorization has motivated several models of natural vision and object recognition. The conclusion of these models is rather far-reaching: humans achieve rapid categorization in a way similar to these models. Since understanding the computations underlying rapid categorization is important for achieving natural vision, we have re-examined several of these models. In particular, we trained the models and tested them with scenes in which the objects to be categorized were replaced with uniform ellipses. We found that the models categorized most of the scenes with ellipses as having the objects. Therefore, these models do not categorize objects but rather the contexts in which the objects are imbedded and thus provide little clue on how humans achieve rapid categorization. Here, we propose a statistical object recognition model based on a large set of hierarchically organized structures of natural objects. First, a large set of hierarchical object structures are obtained from natural objects. At each level of the hierarchy are a set of object structures, each of which consists of a combination of independent components of natural objects. Each object/category is then represented by a subset of these hierarchical structures and the natural variations of the object/category by a probability distribution of the underlying structures. Object recognition/categorization is performed as statistical inference. We tested this model on several large datasets and found that the model achieves a great performance on object recognition/categorization both in isolation and in natural contexts.

He, X. Tsien, J. Yang, Z. (2010). Object recognition based on hierarchically organized structures of natural objects [Abstract]. Journal of Vision, 10(7):989, 989a, http://www.journalofvision.org/content/10/7/989, doi:10.1167/10.7.989. [CrossRef]
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×