Spatial pyramid alignment for sparse coding based object classification

The bag of visual words (BOW) model is widely used for image representation and classification. Spatial pyramid based feature pooling utilizes the BOW model and is the most popular approach to capture the spatial distribution (layout) of local image features. It makes the assumption that the center of an object is aligned with the center of an image, which can lead to misalignment and degradation in performance. In this paper, we propose a method to utilize max pooled features to estimate objects centers and align the spatial pyramid accordingly. We also propose an image representation descriptor robust to misalignments and objects deformations. The experimental results demonstrate that our spatial pyramid alignment method is simple yet efficient in handling misalignments and achieves high object classification accuracy.