Scale invariant texture analysis using multi-scale local autocorrelation features

Yousun Kang, Ken'ichi Morooka, Hiroshi Nagahashi

研究成果: ジャーナルへの寄稿会議記事査読

10 被引用数 (Scopus)

抄録

We have developed a new framework for scale invariant texture analysis using multi-scale local autocorrelation features. The multi-scale features are made of concatenated feature vectors of different scales, which are calculated from higher-order local autocorrelation functions. To classify different types of textures among the given test images, a linear discriminant classifier (LDA) is employed in the multi-scale feature space. The scale rate of test patterns in their reduced subspace can also be estimated by principal component analysis (PCA). This subspace represents the scale variation of each scale step by principal components of a training texture image. Experimental results show that the proposed method is effective in not only scale invariant texture classification including estimation of scale rate, but also scale invariant segmentation of 2D image for scene analysis.

本文言語英語
ページ(範囲)363-373
ページ数11
ジャーナルLecture Notes in Computer Science
3459
DOI
出版ステータス出版済み - 2005
イベント5th International Conference on Scale Space and PDE Methods in Computer Vision, Scale-Space 2005 - Hofgeismar, ドイツ
継続期間: 4月 7 20054月 9 2005

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

フィンガープリント

「Scale invariant texture analysis using multi-scale local autocorrelation features」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル