It is common sense among experts that visual attention plays an important role in perception, being necessary for obtaining salient information about the surroundings. It may be the “glue” that binds simple visual features into an object [1]. Having proposed a spatiotemporal model for visual attention in the past, we elaborate on this work and use it for video classification. Our claim is that simple visual features bound to spatiotemporal salient regions will better represent the video content. Hence, we expect that feature vectors extracted from these regions will enhance the performance of the classifier. We present statistics on sports sequences of five different categories that verify our claims.