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Fast-Learning Adaptive Subspace-Self-Organizing Map for Saliency-Based Invariant Image Feature Construction

This paper presents a methodology using the Adaptive Subspace-Self-Organizing Map (ASSOM) for fast learning and saliency-based invariant feature construction in image classification. The ASSOM algorithm reduces dimensionality and generates invariant features, making it useful for visualization. The experiments demonstrate promising performance in image classification.

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Fast-Learning Adaptive Subspace-Self-Organizing Map for Saliency-Based Invariant Image Feature Construction

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  1. Fast-Learning Adaptive-Subspace Self-Organizing Map: An Application to Saliency-BasedInvariant Image Feature Construction Presenter : You Lin Chen Authors : Huicheng Zheng, Member, IEEE, Grégoire Lefebvre, and Christophe Laurent 2007.WI.7

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. Motivation The traditional learning procedure of the ASSOM involves computations related to a rotation operator matrix. The rotation computations which not only is memory demanding, but also has computational load quadratic to the input dimension.

  4. 2 4.8 3.3 22+4.82+3.32

  5. Objectives In this paper will show that in the ASSOM learning which leads to a computational load linear to both the input dimension and the subspace dimension. we are also interested in applying ASSOM to saliency-based invariant feature construction for image classification.

  6. Methodology_1 Kohonen’s ASSOM learning algorithm

  7. Methodology_1

  8. Methodology_1 Robbins–Monro stochastic approximation

  9. Methodology_1 BFL-ASSOM ASSOM BFL-ASSOM FL-ASSOM

  10. Experiments_1 The input episodes are generated by filtering a whitenoise image with a second-order Butterworth filter. The cutoff frequency isset to 0.6 times the Nyquist frequency of the sampling lattice.

  11. Experiments_1

  12. Methodology_2

  13. Methodology_2

  14. Experiments_2

  15. Conclusion • The ASSOMis useful for dimension reduction, invariant feature generation, and visualization. • BFL-ASSOM, where the increment of each basis vector is a linear combination of the component vectors in the input episode. • The SPMAS showed promising performance on a ten-category image classification problem

  16. Comments • Advantage • … • Drawback • … • Application • …

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