Title :
Unsupervised feature reduction in image segmentation by local Karhunen-Loeve transform
Author_Institution :
Signal Process. Lab., Swiss Federal Inst. of Technol., Lausanne, Switzerland
fDate :
30 Aug-3 Sep 1992
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;
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
DOI :
10.1109/ICPR.1992.201726
