Feature pooling for small visual dictionaries

Abstract

Large visual dictionaries are often used to achieve good image classification performance in bag-of-features (BoF) model, while they lead to high computational cost on dictionary learning and feature coding. In contrast, using small dictionaries can largely reduce the computational cost but result in poor classification performance. Some works have pointed out that pooling locally across feature space can boost the classification performance especially for small dictionaries. Following this idea, various pooling strategies have been proposed in recent years, but they are not good enough for small dictionaries. In this paper, we present a unified framework of pooling operation, and propose two novel pooling strategies to improve the performance of small dictionaries with low extra computational cost. Experimental results on two challenging image classification benchmarks show that our pooling strategies outperform others in most cases.