Svm based indoor/mixed/outdoor classification for digital photo annotation in a ubiquitous computing environment

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)757-767
Number of pages11
JournalComputing and Informatics
Volume27
Issue number5
StatePublished - 2008

Keywords

  • Image classification
  • Low-level feature extraction
  • Support vector machine

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