Locally aggregated histogram-based descriptors

Histogram is commonly used in the area of designing features. However, most existing histogram-based descriptors ignore the information of the distribution of points in each bin. Motivated by VLAD, we introduce the locally aggregation strategy into the design of hand-crafted features to address this issue, and put forward several locally aggregated histogram-based descriptors, including LA-HOG, LA-HOF and LA-MBH, based on HOG, HOF and MBH, respectively. In the binning process of the proposed descriptors, we accumulate the differences between the local information and their nearest bin centers, which describes the distribution of the local information in each bin. The proposed descriptors are utilized in object and action recognition tasks, which are demonstrated to be complementary to the original descriptors in the experiments. The comparison results show that their combinations outperform the original descriptors alone by about 2% on average both in these two tasks.