{"title":"Texture image recognition based on bispectrum slice and BP neural network ensembles","authors":"Zhengjian Ding, Yasheng Yu","doi":"10.1109/ICICISYS.2010.5658582","DOIUrl":null,"url":null,"abstract":"To obtain the spatial relationship between three or more pixels in the texture image, bispectrum is choosen to extract texture features of the image, and it contains amplitude information and phase information of the image. Due to some problems in neural network, such as unstable classifier design, configuration, training, the research based on the ensemble of neural networks appears. Compared with a single neural network, an ensemble of neural networks has better fault tolerance and generalisation ability. In this paper, bispectrum is used to extract texture features and the neural network ensembles are used to recognize the texture images. The experimental results demonstrate that the ensemble of BP neural networks can effectively improve correct recognition rate of texture images.","PeriodicalId":339711,"journal":{"name":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Intelligent Computing and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICISYS.2010.5658582","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
To obtain the spatial relationship between three or more pixels in the texture image, bispectrum is choosen to extract texture features of the image, and it contains amplitude information and phase information of the image. Due to some problems in neural network, such as unstable classifier design, configuration, training, the research based on the ensemble of neural networks appears. Compared with a single neural network, an ensemble of neural networks has better fault tolerance and generalisation ability. In this paper, bispectrum is used to extract texture features and the neural network ensembles are used to recognize the texture images. The experimental results demonstrate that the ensemble of BP neural networks can effectively improve correct recognition rate of texture images.