Subjective evaluation criterion for selecting affective features and modeling highlights
Liyuan Xing, Hua Xing Yu, Qingming Huang, Qixiang Ye, Ajay Divakaran · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
In this paper, we propose a subjective evaluation criterion which is a guide for selecting affective features and modeling highlights. Firstly, the database of highlights ground truth is established, and both the randomness of the data set and the preparation of the subjects are considered. Secondly, commonly used affective features including visual, audio and editing features are extracted to express the highlights. Thirdly, subjective evaluation criterion is proposed based on the analysis of the average error method and pairwise comparisons method, especially the rationality of this criterion in our specific application is explained clearly according to the three detailed issues. Finally, evaluation experiments are designed on tennis and table tennis as examples. Based on the experiments, we prove that previous works on affective features and linear model highlights are effective. Furthermore, 82.0% (79.3%) affective accuracy is obtained fully automatically by computer which is a marvelous highlights ranking result. This result shows the subjective evaluation criterion is well designed for selecting affective features and modeling highlights.