Early delineation of the most salient portions of a temporal image stream (e.g., a video) could serve to guide subsequent processing to the most important portions of the data at hand. Toward such ends, the present paper documents an algorithm for spatiotemporal salience detection. The algorithm is based on a definition of salient regions as those that differ from their surrounding regions, with the individual regions characterized in terms of 3D, (x,y,t), measurements of visual spacetime orientation. The algorithm has been implemented in software and evaluated empirically on a publically available database for visual salience detection. The results show that the algorithm outperforms a variety of alternative algorithms and even approaches human performance.
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