A novel hierarchical Bag-of-Words model for compact action representation

Bag-of-Words (BoW) histogram of local space-time features is very popular for action representation due to its high compactness and robustness. However, its discriminant ability is limited since it only depends on the occurrence statistics of local features. Alternative models such as Vector of Locally Aggregated Descriptors (VLAD) and Fisher Vectors (FV) include more information by aggregating high-dimensional residual vectors, but they suffer from the problem of high dimensionality for final representation. To solve this problem, we novelly propose to compress residual vectors into low-dimensional residual histograms by the simple but efficient BoW quantization. To compensate the information loss of this quantization, we iteratively collect higher-order residual vectors to produce high-order residual histograms. Concatenating these histograms yields a hierarchical BoW (HBoW) model which is not only compact but also informative. In experiments, the performances of HBoW are evaluated on four benchmark datasets: HMDB51, Olympic Sports, UCF Youtube and Hollywood2. Experiment results show that HBoW yields much more compact action representation than VLAD and FV, without sacrificing recognition accuracy. Comparisons with state-of-the-art works confirm its superiority further.