Fast feature extraction approach for multi-dimension feature space problems

Alaa Sagheer, Naoyuki Tsuruta, Rin Ichiro Taniguchi, Daisaku Arita, Sakashi Maeda

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

3 Citations (Scopus)

Abstract

Recently, we proposed a fast feature extraction approach denoted FSOM utilizes Self Organizing Map (SOM). FSOM [1] overcomes the slowness of traditional SOM search algorithm. We investigated the superiority of the new approach using two lip reading data sets which require a limited feature space as the experiments in [1] showed. In this paper, we continue FSOM investigation but using an RGB face recognition database across different poses and different lighting conditions. We believe that such data sets require multi-dimensional feature space to extract the information included in the original data in an effective way especially if you have a big number of classes. Again, we show here how is FSOM reduces the feature extraction time of traditional SOM drastically while preserving same SOM's qualities.

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages417-420
Number of pages4
DOIs
Publication statusPublished - 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: Aug 20 2006Aug 24 2006

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume3
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period8/20/068/24/06

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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  • Cite this

    Sagheer, A., Tsuruta, N., Taniguchi, R. I., Arita, D., & Maeda, S. (2006). Fast feature extraction approach for multi-dimension feature space problems. In Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006 (pp. 417-420). [1699553] (Proceedings - International Conference on Pattern Recognition; Vol. 3). https://doi.org/10.1109/ICPR.2006.545