In this paper, we describe our model designed for automatic prediction of emotional impact of movies. Specifically, a two-stage learning framework is proposed. First, the dimensionality reduction techniques are employed to discover the key emotion information embedded in the original feature space. Specifically, we use a classical method principal component analysis (PCA) and a new algorithm biased discriminant embedding (BDE) to learn the subspace. After dimensionality reduction, SVM is utilized for prediction. Experimental results validate the effectiveness of our approaches.
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 2017|
|Event||2017 Multimedia Benchmark Workshop, MediaEval 2017 - Dublin, Ireland|
Duration: 13 Sep 2017 → 15 Sep 2017
Scopus Subject Areas
- Computer Science(all)