并不是所有的训练图像都有帮助对提高AIA方法的泛化和性能。为了使所提出

并不是所有的训练图像都有帮助对提高AIA方法的泛化和性能。为了使所提出的AIA方法能够更适应大规模图像环境，本文将采用样本选择技术来剔除无用的学习样本。

結果 (英文) 1:

Not all methods of training images has helped to improve the AIA extension and performance. In order to make the method of AIA environment can accommodate massive image, this article will use the sample selection technology to weed out the useless learning sample.

結果 (英文) 2:

Not all training images have helped to improve the AIA method of generalization and performance. AIA in order to make the proposed method can adapt to more large-scale image environment, this article will use the sample selection techniques to remove unwanted learning samples.

結果 (英文) 3:

Not all of the training images have helped to improve the generalization and performance of the AIA method. In order to make the proposed AIA method more suitable for large-scale image environment, this paper will use the sample selection technique to eliminate the useless learning samples.