An improved random forest classifier for image classification
文献类型：会议论文
| 作者 | Baoxun Xu; Yunming Ye; Lei Nie |
| 出版日期 | 2012 |
| 会议名称 | Information and Automation (ICIA), 2012 International Conference on |
| 会议地点 | 中国 |
| 英文摘要 | This paper proposes an improved random forest algorithm for image classification. This algorithm is particularly designed for analyzing very high dimensional data with multiple classes whose well-known representative data is image data. A novel feature weighting method and tree selection method are developed and synergistically served for making random forest framework well suited to classify image data with a large number of object categories. With the new feature weighting method for subspace sampling and tree selection method, we can effectively reduce subspace size and improve classification performance without increasing error bound. Experimental results on image datasets with diverse characteristics have demonstrated that the proposed method could generate a random forest model with higher performance than the random forests generated by Breiman's method. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/3923]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2012 |
| 推荐引用方式 GB/T 7714 | Baoxun Xu,Yunming Ye,Lei Nie. An improved random forest classifier for image classification[C]. 见:Information and Automation (ICIA), 2012 International Conference on. 中国. |
入库方式： OAI收割
来源：深圳先进技术研究院
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