Empirical Mode Decomposition for rotation invariant texture classification

A novel and effective scheme for rotation invariant texture classification is presented using an adaptive and approximately orthogonal filtering process-bidimensional empirical mode decomposition (BEMD). The extraction of rotation invariant feature for a given image involves BEMD and circular zones....

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Bibliographic Details
Main Authors: Xiong Changzhen, Guo Fenhong
Format: Conference Proceeding
Language:English
Subjects:
Adaptive filters
Data mining
Discrete wavelet transforms
Feature extraction
Frequency
Gabor filters
Image processing
Intelligent transportation systems
Signal processing
Wavelet packets
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Summary:A novel and effective scheme for rotation invariant texture classification is presented using an adaptive and approximately orthogonal filtering process-bidimensional empirical mode decomposition (BEMD). The extraction of rotation invariant feature for a given image involves BEMD and circular zones. A feature vector extracted from circular zones of intrinsic mode function (IMF) is constructed for rotation invariant texture classification. In the experiments, we use rotation invariant feature to classify a set of 25 distinct natural textures selected from the Brodatz album. The experimental results show that the effectiveness of the proposed classification scheme compared with other classification methods.
ISSN:1555-5798
2154-5952
DOI:10.1109/PACRIM.2009.5291310