计算机科学 ›› 2014, Vol. 41 ›› Issue (2): 161-165.

• CCML 2013 • 上一篇    下一篇

基于改进模糊综合评价的电影情感分类

林新棋   

  1. 福建师范大学数学与计算机科学学院 福州350007 福建师范大学网络安全与密码技术福建省高效重点实验室 福州350007
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61070062,1),福建省教育厅基金项目(JA12075,JA10064,JB11036),福建省高等学校科技创新团队(IRTSTFJ,N.J1917)资助

Film Affective Classification Based on Improved Fuzzy Comprehensive Evaluation

LIN Xin-qi   

  • Online:2018-11-14 Published:2018-11-14

摘要: 为了提高电影情感分类精确度,以模糊数学理论为基础,建立电影底层特征和高层认知情感之间的关系,提出一种基于改进模糊综合评价的电影情感分类新算法。首先,选择了反映情感变化的场景亮度、镜头切换率和颜色能量作为视频场景底层特征,介绍了相应的特征提取方法。其次,引入和改进模糊综合评价模型,并给出特征对情感的模糊隶属函数,建立了单因素评价矩阵。最后,对于不同情感,采用层次分析法确定底层特征之间的相对权重,根据改进的模糊综合评价模型计算出电影场景的情感模糊特征向量,用最大判决值和阈值原则确定待识别场景的情感类型。实验结果表明,所提出的算法能有效地提高电影场景的情感分类精确度。

关键词: 情感模糊特征向量,模糊综合评价,单因素评价矩阵,视频情感内容 中图法分类号TP391.4文献标识码A

Abstract: In order to improve the classification accuracy of the film scene emotion,a novel algorithm was proposed based on the improved fuzzy comprehensive evaluation in the fuzzy mathematics theory by establishing the relationship between the low-level features and high-level cognitive emotion.First,the scene luminance,shot cut rates and color ener-gy were selected as the low-level features for theirs special characteristics that can be used to better distinguish different types of human emotional reaction.Further,the extractive methods were put forward.Secondly,after introducing and improving the fuzzy comprehensive evaluation model,fuzzy membership functions were formed to measure the fuzzy relationship between low-level features and emotion,and then the single factor evaluation matrix was built.Finally,the method of the analytic hierarchy process (AHP) was used to determine the relative weight matrix between the features,and the affective fuzzy feature vector was computed by the improved fuzzy comprehensive evaluation model.And the affective type of the film scene was obtained by the maximization value of the components of the affective fuzzy feature vector and threshold at last.The experimental results show that the proposed algorithm can effectively improve the accuracy of the film affective classification.

Key words: Affective fuzzy feature vector,Fuzzy comprehensive evaluation,Single factor evaluation matrix,Video affective content

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