Image classification using wavelet coefficients in low-pass bands

Zou, Weibao and Li, Yan (2007) Image classification using wavelet coefficients in low-pass bands. In: 2007 International Joint Conference on Neural Networks (IJCNN), 12-17 August 2007, Orlando, Florida, USQ.

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Abstract

[Abstract]: In this paper, a method based on wavelet coefficients in low-pass bands is proposed for the image classification with adaptive processing of data structures to organize a large image database. After an image is decomposed by wavelet, its features can be characterized by the distribution of histograms of wavelet coefficients. The coefficients are respectively projected onto x and y directions. For different images, the distribution of histograms of wavelet coefficients in low-pass bands is substantially different. However, the one in high-pass bands is not as different, which makes the performance of classification not reliable, This paper presents a method for image classification based on wavelet coefficients in low-pass bands only. Images are arranged into a tree structure. The nodes can then be represented by the distribution of histograms of these wavelet coefficients. 2940 images derived from seven categories are used for image classification. Based on the wavelet coefficients in low-pass bands, the improvement of classification rate on the training data set is up to 11%, and the improvement of classification rate on the testing data set reaches 20%. Experimental results show that our proposed approach for image classification is more effective and reliable.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Speech)
Refereed: Yes
Item Status: Live Archive
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Depositing User: Dr Yan Li
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 14 Feb 2008 06:07
Last Modified: 02 Jul 2013 22:58
Uncontrolled Keywords: wavelet decomposition, classification
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
URI: http://eprints.usq.edu.au/id/eprint/3850

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