UNIFESP at MediaEval 2016: Predicting Media Interestingness Task
نویسنده
چکیده
This paper describes the approach proposed by UNIFESP for the MediaEval 2016 Predicting Media Interestingness Task and for its video subtask only. The proposed approach is based on combining learning-to-rank algorithms for predicting the interestingness of videos by their visual content.
منابع مشابه
Technicolor@MediaEval 2016 Predicting Media Interestingness Task
This paper presents the work done at Technicolor regarding the MediaEval 2016 Predicting Media Interestingness Task, which aims at predicting the interestingness of individual images and video segments extracted from Hollywood movies. We participated in both the image and video subtasks.
متن کاملLAPI at MediaEval 2016 Predicting Media Interestingness Task
This paper will present our results for the MediaEval 2016 Predicting Media Interestingness task. We proposed an approach based on video descriptors and studied several machine learning models, in order to detect the optimal configuration and combination for the descriptors and algorithms that compose our system.
متن کاملNII-UIT at MediaEval 2016 Predicting Media Interestingness Task
The MediaEval 2016 Predicting Media Interestingness (PMI) Task requires participants to retrieve images and video segments that are considered to be the most interesting for a common viewer. This is a challenging problem not only because the large complexity of the data but also due to the semantic meaning of interestingness. This paper provides an overview of our framework used in MediaEval 20...
متن کاملTUD-MMC at MediaEval 2016: Predicting Media Interestingness Task
This working notes paper describes the TUD-MMC entry to the MediaEval 2016 Predicting Media Interestingness Task. Noting that the nature of movie trailer shots is different from that of preceding tasks on image and video interestingness, we propose two baseline heuristic approaches based on the clear occurrence of people. MAP scores obtained on the development set and test set suggest that our ...
متن کاملRUC at MediaEval 2016: Predicting Media Interestingness Task
Measuring media interestingness has a wide range of applications such as video recommendation. This paper presents our approach in the MediaEval 2016 Predicting Media Interestingness Task. There are two subtasks: image interestingness prediction and video interestingness prediction. For both subtasks, we utilize hand-crafted features and CNN features as our visual features. For the video subtas...
متن کامل