Frequency and Space Domain Features for Image Classification Using Gaussian Mixture Models

摘要：

This paper presents an effective combination of Wavelet-based features and SIFT features,both of them have the frequency domain and space domain information characteristic.For the combined feature patches extracted from images we then adopt the PCA transformation to reduce the dimensionality of their feature vectors. And the reduced vectors are used to train Gaussian Mixture Models (GMMs) in which the mixture weights are adjusted iteratively. We experiment on Caitech datasets using this enhanced method,and the results comparing with several other methods show that the combination of salient feature vectors and GMM gives a much better improvement in image classification.

关键词： Image Classification Gaussian mixture models Feature extraction

作者: Bin Fu Zhen Ren

作者单位: Pattern Recognition Laboratory College of A utomation Harbin Engineering University

会议类型: 国际会议

会议名称: The 2008 International Conference on Embedded Software and Systems Symposia(ICESS 2008)(2008国际嵌入式系统及嵌入式软件会议)

会议地点: 成都

会议语种:英文

页码: 441-446

在线出版日期: 2008-01-01（万方平台首次上网日期，不代表论文的发表时间）