Detecting image orientation based on low-level visual content

Yongmei Michelle Wang, Hongjiang Zhang

Research output: Contribution to journalArticlepeer-review


Accurately and automatically detecting image orientation is of great importance in intelligent image processing. In this paper, we present automatic image orientation detection algorithms based on both the luminance (structural) and chrominance (color) low-level content features. The statistical learning support vector machines (SVMs) are used in our approach as the classifiers. The different sources of the extracted image features, as well as the binary classification nature of SVM, require our system to be able to integrate the outputs from multiple classifiers. Both static combiner (averaging) and trainable combiner (also based on SVMs) are proposed and evaluated in this work. Furthermore, two rejection options (regular and re-enforced ambiguity rejections) are employed to improve orientation detection accuracy by sieving out images with low confidence values during the classification. Large amounts of experiments have been conducted on a database of more than 14,000 images to validate our approaches. Discussions and future directions for this work are also addressed at the end of the paper.

Original languageEnglish (US)
Pages (from-to)328-346
Number of pages19
JournalComputer Vision and Image Understanding
Issue number3
StatePublished - Mar 2004
Externally publishedYes


  • Classifier combination
  • Image database
  • Image orientation
  • Low-level visual content
  • Pattern classification
  • Support vector machine

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering


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