Abstract: Bag-of-words image representation has shown to be a powerful technique for image classification，which creates a signature for each image using visual words（image features） in the pre-defined codebook.This paper proposes a new statistical approach to discover the discriminability of each visual word for each image category first，and then a general linear model（GLM） is employed to combine these visual words to construct new signatures of the images.Experimental results show that the approach can enhance the discriminability of each signature and thus improve the performance of image classification.

摘要： Bag-of-Words模型对于图片分类来说是一种非常有用的技术，它利用事先定义好的“可见字”为每张图片建立一个特征向量。提出了一种新的统计方法来发掘可见字对于每一类图片的区分能力，再利用线性模型合并“可见字”，从而为每张图片构造新的特征向量。实验结果显示这一算法能够增强特征向量的区分度，进而提高图片分类的性能。