Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

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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

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Outline

Motivation

Objective

Methodology

Experiments

Conclusion

Comments

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I. M.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.

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2 4.

83.3

22+4.82+3.32

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I. M.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.

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I. M.Methodology_1

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Kohonen’s ASSOM learning algorithm

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I. M.Methodology_1

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Robbins–Monro stochastic

approximation

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I. M.Methodology_1

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BFL-ASSOM

FL-ASSOMBFL-ASSOM

ASSOM

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Experiments_1

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

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I. M.Experiments_1

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I. M.Methodology_2

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I. M.Experiments_2

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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

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Comments

Advantage …

Drawback …

Application …