  Selecting attractive photos from a human action shot sequence is quite
challenging, because of the subjective nature of the "attractiveness", which is
mainly a combined factor of human pose in action and the background. Prior
works have actively studied high-level image attributes including
interestingness, memorability, popularity, and aesthetics. However, none of
them has ever studied the "attractiveness" of human action shot. In this paper,
we present the first study of the "attractiveness" of human action shots by
taking a systematic data-driven approach. Specifically, we create a new
action-shot dataset composed of about 8000 high quality action-shot photos. We
further conduct rich crowd-sourced human judge studies on Amazon Mechanical
Turk(AMT) in terms of global attractiveness of a single photo, and relative
attractiveness of a pair of photos. A deep Siamese network with a novel hybrid
distribution matching loss was further proposed to fully exploit both types of
ratings. Extensive experiments reveal that (1) the property of action shot
attractiveness is subjective but predicable (2) our proposed method is both
efficient and effective for predicting the attractive human action shots.
