关键词: 时空金字塔, 偏最小二乘法, 稀疏编码, 池化技术, 底层特征

Abstract: In Spatio-Temporal Pyramid Representation（STPR）, the video is divided into a series of increasingly finer cubic unit cells on each pyramid level. Local features are extracted from all of the cubic unit cells and are concatenated to rebuild a high dimensional feature vector. As a result, when the samples are trained and tested, high computational costs are required. Moreover, because the partitioning strategy for the video is divided into parts which is designed by hand, there is poor theoretical evidence for the optimal partitioning strategy for good behavior recognition. This paper proposes discriminative STPR, which is a new representation that constructs the video feature as a weighted sum of semi-local features over all pyramid levels. This weights are automatically calculated by using partial least square method to maximize a discriminative power. The resulting representation is compact and reserves high discriminative power. Furthermore, this representation can reveal the distinctive cubic unit cells and the number of pyramid level simultaneously by observing the optimal weights of cubic unit cells generated from the fine cubic unit cells.

Key words: Spatio-Temporal Pyramid Representation（STPR）, Partial Least Square（PLS） method, sparse coding, pooling, low-level feature