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EigenSegments: A Spatio-Temporal Decomposition of an Ensemble of Images

  • Shai Avidan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2352)

Abstract

Eigensegments combine image segmentation and Principal Component Analysis (PCA) to obtain a spatio-temporal decomposition of an ensemble of images. The image plane is spatially decomposed into temporally correlated regions. Each region is independently decomposed temporally using PCA. Thus, each image is modeled by several low-dimensional segment-spaces, instead of a single high-dimensional image-space. Experiments show the proposed method gives better classification results, gives smaller reconstruction errors, can handle local changes in appearance and is faster to compute. Results for faces and vehicles are shown.

Keywords

Principal Component Analysis Support Vector Machine Test Image Receiver Operator Characteristic Reconstruction Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Shai Avidan
    • 1
  1. 1.Kanfei NesharimThe Interdisciplinary CenterHerzliyaIsrael

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