一种基于视觉显著度词袋模型的图像分类方法

An Image Classification Based on Saliency Map and Bag of word model

本文在传统词袋模型的基础上,结合人的视觉特性,提出了一种基于视觉显著度与词袋模型的图像分类方法。算法首先计算图像的视觉显著度,然后根据图像的视觉显著度对图像计算视觉单词的加权直方图,然后使用视觉单词的加权直方图表示图像。通过在Caltech 101数据库进行实验,验证了本文方法的有效性,实验结果表明,该方法能够大幅度提高图像分类的性能。

Based on Bag of Words model,an improved algorithm based saliency is proposed in this paper. Firstly, computing saliency image or＇the original image, according to the saliency＂image, we weighted visual word histogram of the image, and then use the weighted histogram visual to represent the image The experiments arecarried out on Caltech 101 database. The results show that the proposed method performsbetter than the traditional method.