BigVid at MediaEval 2016: Predicting Interestingness in Images and Videos

ABSTRACT Despite growing research interest, the tasks of predicting the interestingness of images and videos remain as an open challenge. The main obstacles come from both the diversity and complexity of video content and highly subjective and varying judgements of interestingness of different persons. In the MediaEval 2016 Predicting Media Interestingness Task, our team of BigVid@Fudan had submitted five runs exploring various methods of extraction, and modeling the low-level features (from visual and audio modalities) and hundreds of high-level semantic attributes; and fusing these features for classification. We not only investigated the use of the SVM (Support Vector Machine) model; but the recent deep learning methods were explored as well. We had submitted 5 runs using SVM/Ranking-SVM (Run1, Run3 and Run4) and Deep Neural Networks (Run2 and Run5) respectively. We achieved a mean average precision of 0.23 for the image subtask and 0.15 for the video subtask. Furthermore, our experiments revealed some insights of this task which are interesting and potential useful. For example, our results show that the visual features and high-level attributes are complementary to each other