spatial pooling for transformation invariant image representation
Spatial Pyramid Matching (SPM) [2] has been proposed to extend the Bag-of-Word (BoW) model for object classification. By re-serving the finer level information, it makes image matching more accurate. However, for not well-aligned images, where the object is rotated, flipped or translated, SPM may lose its discrimination power. To tackle this problem, we propose novel spatial pooling layouts to address various transformations, and generate a more general image representation. To evaluate the effectiveness of the proposed approach, we conduct extensive experiments on three transformation emphasized datasets for object classification task. Experimental results demonstrate its superiority over the state-of-the-arts. Besides, the proposed image representation is compact and consistent with the BoW model, which makes it applicable to image retrieval task as well.
image representation and object classification. spatial transformation