一种融合特征选择的AdaBoost集成算法

针对Ada Boost算法训练分类器的特征具有大量冗余问题,提出了一种融合特征选择的Ada Boost集成算法.首先,使用一种特征选取方法,选择图像特征之间冗余度最小的特征,构造最优训练集;其次,采用Ada Boost算法训练分类器,构建分类模型;最后,使用分类模型实现待标注图像的自动标注.实验使用华盛顿大学用于图像自动标注的数据集,结果验证算法的有效性,并且相比其他传统算法,该算法具有更高的分类精度.

An Ada Boost Algorithm with Integration of Feature Selection

In Ada Boost algorithm,a lot of redundant features existed in the training of the classifier. In allusion to the problem mentioned above,an Ada Boost algorithm with integration of feature selection was proposed. Firstly,a feature selection method was developed for our model. Those visual features with small correlations between them was chosen to establish the optimal training set. Secondly,the Ada Boost integrated classification algorithm was used to train the classifier and to establish the classifier model. Finally,the classifier model mentioned above was used to realize the automatic annotation of the unlabeled images. Based the university of Washington image dataset,the experiment results showed that the proposed feature selection method was very suitable for the classifier,and compared with other classical algorithms,the algorithm given in this paper had the optimal accuracy classification.