In this paper, we describe the models designed for automatically selecting multimedia data, e.g., image and video segments, which are considered to be interesting for a common viewer. Specifically, we utilize an existing dimensionality reduction method called Neighborhood MinMax Projections (NMMP) to extract the low-dimensional features for predicting the discrete interestingness labels. Meanwhile, we introduce a new dimensionality reduction method dubbed Supervised Manifold Regression (SMR) to learn the compact representations for predicting the continuous interestingness levels. Finally, we use the nearest neighbor classifier and support vector regressor for classification and regression, respectively. Experimental results demonstrate the effectiveness of the low-dimensional features learned by NMMP and SMR.
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 2016|
|Event||2016 Multimedia Benchmark Workshop, MediaEval 2016 - Hilversum, Netherlands|
Duration: 20 Oct 2016 → 21 Oct 2016
Scopus Subject Areas
- Computer Science(all)