Abstract:

 Due to the fuzzy nature of the human emotional reaction, it is difficult to improve the accuracy only using the low-level features. Based on the relationships between the affect and low-level features obtained by the previous workers, the fuzzy membership functions were introduced, and the low-level features were processed by fuzzy principle of the maximum membership degree. Then a fuzzy feature vector was obtained, which could imply the affective information of the given video clip, and could be used to recognize the affective type by syllogism inference. The experimental results showed that the fuzzy feature vector could shorten the gap between the affects and low-level contents. The classification accuracies of three affects all exceeded 84％. Compared with the existing methods, the total average classification accuracy increased 9.33％, which proved that the proposed algorithm could effectively improve the accuracy of the film affective classification.

Key words:  film affective content, low-level feature, fuzzy membership function of feature strength, fuzzy feature vector, syllogism inference