Wavelet-Based Image Texture Classification Using Local Energy Histograms

In this letter, we propose an efficient one-nearest-neighbor classifier of texture via the contrast of local energy histograms of all the wavelet subbands between an input texture patch and each sample texture patch in a given training set. In particular, the contrast is realized with a discrepancy...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 18; no. 4; pp. 247 - 250
Main Authors Dong, Yongsheng, Ma, Jinwen
Format Journal Article
LanguageEnglish
Published New York IEEE 01.04.2011
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Classification
Energy histogram
Energy use
Feature extraction
High definition video
Histograms
Indexes
one-nearest-neighbor classifier
State of the art
Surface layer
symmetrized Kullback-Leibler divergence (SKLD)
Texture
texture classification
Training
Wavelet
wavelet transform
Wavelet transforms
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Summary:In this letter, we propose an efficient one-nearest-neighbor classifier of texture via the contrast of local energy histograms of all the wavelet subbands between an input texture patch and each sample texture patch in a given training set. In particular, the contrast is realized with a discrepancy measure which is just a sum of symmetrized Kullback-Leibler divergences between the input and sample local energy histograms on all the wavelet subbands. It is demonstrated by various experiments that our proposed method obtains a satisfactory texture classification accuracy in comparison with several current state-of-the-art texture classification approaches.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2011.2111369