GPCA-SIFT: A New Local Feature Descriptor for Scene Image Classification

摘要：

　　In this paper,a new local feature descriptor called GPCASIFT is proposed for scene image classification.Like PCA-SIFT,we get the key points using the detection method in Scale Invariant Feature Transform(SIFT)and extract a 41 * 41 patch for each key point.Then we calculate the horizontal and vertical gradient of each pixel in the patch.However,instead of concatenating two gradient matrices,we directly work with the two-dimensional matrix and apply Generalized Principal Component Analysis(GPCA)to reduce it to a lower-dimensional matrix.Finally,we concatenate the reduced matrix and form a 1D vector.Compared with Principal Component Analysis(PCA),it preserves more spatial locality information.When applied in multi-class scene image classification,our proposed descriptor outperforms other related algorithms in terms of classification accuracy.

关键词： SIFT GPCA GPCA-SIFT Scene image classification

作者: Lei Ju Ke Xie Hao Zheng Baochang Zhang Wankou Yang

作者单位: School of Automation,Southeast University,Nanjing 210096,China;Key Laboratory of Measurement and Con Key Laboratory of Trusted Cloud Computing and Big Data Analysis,Nanjing Xiaozhuang University,Nanjin School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China School of Automation,Southeast University,Nanjing 210096,China;Key Laboratory of Measurement and Con

会议类型: 国际会议

会议名称: 第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)

会议地点: 成都

会议语种:英文

页码: 286-295

在线出版日期: 2016-11-03（万方平台首次上网日期，不代表论文的发表时间）