Boosting image classification with LDA-based feature combination for digital photograph management

  • Authors:

  • Xuezheng Liu;Lei Zhang;Mingjing Li;Hongjiang Zhang;Dingxing Wang

  • Affiliations:

  • Department of Computer Science and Technology, Tsinghua University, Beijing 100080, China;Microsoft Research Asia, 49 Zhichun Road, Beijing 100080, China;Microsoft Research Asia, 49 Zhichun Road, Beijing 100080, China;Microsoft Research Asia, 49 Zhichun Road, Beijing 100080, China;Department of Computer Science and Technology, Tsinghua University, Beijing 100080, China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2005

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Abstract

Image classification is of great importance for digital photograph management. In this paper we propose a general statistical learning method based on boosting algorithm to perform image classification for photograph annotation and management. The proposed method employs both features extracted from image content (i.e., color moment and edge direction histogram) and features from the EXIF metadata recorded by digital cameras. To fully utilize potential feature correlations and improve the classification accuracy, feature combination is needed. We incorporate linear discriminant analysis (LDA) algorithm to implement linear combinations between selected features and generate new combined features. The combined features are used along with the original features in boosting algorithm for improving classification performance. To make the proposed learning algorithm more efficient, we present two heuristics for selective feature combinations, which can significantly reduce training computation without losing performance. The proposed image classification method has several advantages: small model size, computational efficiency and improved classification performance based on LDA feature combination.