Comments on "SVD-Based Modeling for Image Texture Classification Using Wavelet Transform"

This correspondence points out that in there are some errors in two important formulas and the performance of the proposed singular value decomposition (SVD) feature is severely overestimated. It also presents a modified SVD feature which competes with the Gabor feature.

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Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on image processing 2013-12, Vol.22 (12), p.5408-5408
Hauptverfasser:	Kim, Nam Chul, So, Hyun Joo
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Artificial Intelligence Detection, estimation, filtering, equalization, prediction Exact sciences and technology Feature extraction Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Image texture Image texture classification Information, signal and communications theory Kullback-Leibler distance (KLD) Pattern Recognition, Automated - methods Signal and communications theory Signal processing Signal Processing, Computer-Assisted Signal representation. Spectral analysis Signal, noise Singular value decomposition singular value decomposition (SVD) Telecommunications and information theory Training Wavelet transforms
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Beschreibung
Zusammenfassung:	This correspondence points out that in there are some errors in two important formulas and the performance of the proposed singular value decomposition (SVD) feature is severely overestimated. It also presents a modified SVD feature which competes with the Gabor feature.
ISSN:	1057-7149 1941-0042
DOI:	10.1109/TIP.2013.2279319