SVM Based Indoor/Mixed/Outdoor Classification for Digital Photo Annotation in a Ubiquitous Computing Environment

Authors

  • Chull Hwan Song
  • Seong Joon Yoo
  • Chee Sun Won
  • Hyoung Gon Kim

Keywords:

Image classification, support vector machine, low-level feature extraction

Abstract

This paper extends our previous framework for digital photo annotation by adding noble approach of indoor/mixed/outdoor image classification. We propose the best feature vectors for a support vector machine based indoor/mixed/ outdoor image classification. While previous research classifies photographs into indoor and outdoor, this study extends into three types, including indoor, mixed, and outdoor classes. This three-class method improves the performance of outdoor classification. This classification scheme showed 5--10% higher performance than previous research. This method is one of the components for digital image annotation. A digital camera or an annotation server connected to a ubiquitous computing network can automatically annotate captured photos using the proposed method.

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Published

2012-01-26

How to Cite

Song, C. H., Yoo, S. J., Won, C. S., & Kim, H. G. (2012). SVM Based Indoor/Mixed/Outdoor Classification for Digital Photo Annotation in a Ubiquitous Computing Environment. Computing and Informatics, 27(5), 757–767. Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/214