基于词袋模型的迁移学习算法

Novel Transfer Learning Algorithm Based on Bag-of-visual Words Model

在分类新类别图像时,词袋模型总需要重新学习视觉词典及分类器,而不能充分利用已经学习好的视觉词典。运用迁移学习的思想,提出一种视觉短语的迁移学习算法。这种视觉短语不仅包含图像的局部不变特征,而且包含特征间的空间结构信息,能更有效地描述不同类别图像之间的共同特征。在分类新类别图像时,算法通过迁移视觉短语而不是重新学习视觉词典,来完成图像分类任务。实验结果证明这种迁移算法能有效地利用已有知识,在分类新类别图像时取得很好的效果,而且还能适用于仅有少量训练样本的图像分类任务。

The bag-of-visual words model needs to learn visual vocabulary and classifier from the beginning when it learns a novel image category,and it cannot make use of learned visual vocabulary.This paper proposed a transfer learning algorithm based on visual phrases.The visual phrases contain not only local invariable features,but also local spatial information,which can describe the common characteristics among different image categories.Our algorithm can make the bag-of-visual words model obtain good performance in the novel image category by transferring visual phrases from source visual vocabulary.The experimental results validate that the algorithm effectively utilizes learned knowledge and gains better performance in the novel image category even when there are a few training images.