Multi-scale sparse representation based scene categorization
基于多尺度稀疏表示的场景分类

This paper proposed a novel multi-scale sparse representation based framework of scene categorization. Firstly, it extracted local features of multiple scales, then learned dictionaries of each scale by using sparse coding algorithm. In the stage of image representation, this paper proposed to encode each local feature according to dictionary on the same scale. By using spatial pyramid representation and feature max-pooling, it summarized the features on each scale. In the final step, the proposed algorithm concatenated the pooled features of different scales to form the holistic feature. The experimental results on three popular benchmark datasets show that since the proposed method utilizes the complementary relationship of features on different scales, it obtains better performance compared with methods using features on single scale.