Article thumbnail
Location of Repository

Contextual-Guided Bag-of-Visual-Words Model for Multi-class Object Categorization

By Mehdi Mirza-mohammadi, Sergio Escalera and Petia Radeva

Abstract

Abstract. Bag-of-words model (BOW) is inspired by the text classifi-cation problem, where a document is represented by an unsorted set of contained words. Analogously, in the object categorization problem, an image is represented by an unsorted set of discrete visual words (BOVW). In these models, relations among visual words are performed after dic-tionary construction. However, close object regions can have far descrip-tions in the feature space, being grouped as different visual words. In this paper, we present a method for considering geometrical information of visual words in the dictionary construction step. Object interest regions are obtained by means of the Harris-Affine detector and then described using the SIFT descriptor. Afterward, a contextual-space and a feature-space are defined, and a merging process is used to fuse feature words based on their proximity in the contextual-space. Moreover, we use the Error Correcting Output Codes framework to learn the new dictionary in order to perform multi-class classification. Results show significant clas-sification improvements when spatial information is taken into account in the dictionary construction step.

Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.454.3227
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://www.maia.ub.es/~sergio/... (external link)
  • http://www.maia.ub.es/~sergio/... (external link)
  • http://citeseerx.ist.psu.edu/v... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.