Supervised manifold learning for media interestingness prediction

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
JournalCEUR Workshop Proceedings
Publication statusPublished - 22 Nov 2016
Event2016 Multimedia Benchmark Workshop, MediaEval 2016 - Hilversum, Netherlands
Duration: 20 Oct 201621 Oct 2016

Scopus Subject Areas

  • Computer Science(all)


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