DocumentCode
3057220
Title
Unsupervised feature reduction in image segmentation by local Karhunen-Loeve transform
Author
Bigün, Josef
Author_Institution
Signal Process. Lab., Swiss Federal Inst. of Technol., Lausanne, Switzerland
fYear
1992
fDate
30 Aug-3 Sep 1992
Firstpage
79
Lastpage
83
Abstract
Proposes to reduce the dimensionality of feature vectors by using the principles of Karhunen-Loeve transform, (KL) applied to the feature images locally and globally. The reduction is achieved by choosing the resulting basis vectors which are closest to those of the classical KL transform. An efficient implementation technique using pyramids is proposed. Experimental results are presented
Keywords
feature extraction; image recognition; image segmentation; transforms; Karhunen-Loeve transform; dimensionality; feature vectors; image processing; image segmentation; machine vision; unsupervised feature reduction; Acoustic testing; Discrete transforms; Electrical capacitance tomography; Feature extraction; Image segmentation; Karhunen-Loeve transforms; Laboratories; Mean square error methods; Signal processing; Signal processing algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1992. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on
Conference_Location
The Hague
Print_ISBN
0-8186-2915-0
Type
conf
DOI
10.1109/ICPR.1992.201726
Filename
201726
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