Region-Based Spatial Sampling for Image Classification

Local descriptors with Bag-of-Words representation were widely used for image classification. Especially, local descriptors of dense spatial sampling were demonstrated to be able to further improve performances of image classification. However, denser spatial sampling is impractical due to huge computation cost. To handle this issue, we propose a new region-based sampling strategy in this paper. We first perform an over-segmentation to get image regions, and then we extract local descriptors around the region boundaries and inside the regions respectively with a popular sampling strategy. Thus, almost at the same computation cost, the proposed method can capture more salient points and obtain much better classification performance. Extensive experiments are conducted on two widely-used datasets (UIUC Sports and Caltech-101). The experimental results demonstrate the effectiveness of the proposed method. Specifically, the proposed method updates the state-of-the-art on UIUC Sports dataset with a classification accuracy of 89.38%.