Abstract
A real world scene may contain several objects with different spatial and temporal characteristics. This paper proposes a novel method for the classification of natural scenes by processing both spatial and temporal information from the video. For extracting the spatial characteristics, we build spatial pyramids using the spatial pyramid matching (SPM) algorithm on SIFT descriptors while for the motion characteristics, we introduce a five dimensional feature vector extracted from the optical flow field. We employ SPM on combined SIFT and motion feature descriptors to perform classification. We demonstrate that the proposed approach shows significant improvement in scene classification as compared to the SPM algorithm on SIFT spatial feature descriptors alone.