Image classification using wavelet coefficients in low-pass bands
Paper
Paper/Presentation Title | Image classification using wavelet coefficients in low-pass bands |
---|---|
Presentation Type | Paper |
Authors | Zou, Weibao (Author) and Li, Yan (Author) |
Editors | Si, Jennie |
Journal or Proceedings Title | Proceedings of 2007 International Joint Conference on Neural Networks (IJCNN) |
Number of Pages | 5 |
Year | 2007 |
Place of Publication | United States |
ISBN | 9781424413799 |
9781424413805 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/IJCNN.2007.4370940 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/abstract/document/4370940 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/4370890/proceeding |
Conference/Event | 2007 International Joint Conference on Neural Networks (IJCNN) |
Event Details | 2007 International Joint Conference on Neural Networks (IJCNN) Parent International Joint Conference on Neural Networks (IJCNN) Event Date 12 to end of 17 Aug 2007 Event Location Orlando, United States of America |
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. |
Keywords | wavelet decomposition, classification |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460306. Image processing |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | Chinese Academy of Sciences, China |
Department of Mathematics and Computing |
https://research.usq.edu.au/item/9y965/image-classification-using-wavelet-coefficients-in-low-pass-bands
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