Multiresolution techniques for the classification of bioimage and biometrie datasets

Amina Chebira, Jelena Kovačević

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We survey our work on adaptive multiresolution (MR) approaches to the classification of biological and fingerprint images. The system adds MR decomposition in front of a generic classifier consisting of feature computation and classification in each MR subspace, yielding local decisions, which are then combined into a global decision using a weighting algorithm. The system is tested on four different datasets, subcellular protein location images, drosophila embryo images, histological images and fingerprint images. Given the very high accuracies obtained for all four datasets, we demonstrate that the space-frequency localized information in the multiresolution subspaces adds significantly to the discriminative power of the system. Moreover, we show that a vastly reduced set of features is sufficient. Finally, we prove that frames are the class of MR techniques that performs the best in this context. This leads us to consider the construction of a new family of frames for classification, which we term lapped tight frame transforms.

Original languageEnglish (US)
Title of host publicationWavelets XII
DOIs
StatePublished - 2007
EventWavelets XII - San Diego, CA, United States
Duration: Aug 26 2007Aug 29 2007

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6701
ISSN (Print)0277-786X

Other

OtherWavelets XII
CountryUnited States
CitySan Diego, CA
Period8/26/078/29/07

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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