论文信息 - LAPI at MediaEval 2017 - Predicting Media Interestingness

LAPI at MediaEval 2017 - Predicting Media Interestingness

In the following paper we will present our contribution, approach and results for the MediaEval 2017 Predicting Media Interestingness task. We studied several visual descriptors and created several early and late fusion approaches in our machine learning system, optimized for best results for this benchmarking competition.

[1] Abhisek Ukil,et al. Support Vector Machine , 2007 .

[2] Tsuhan Chen,et al. > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

[3] Bogdan Ionescu,et al. Content description for Predicting image Interestingness , 2017, 2017 International Symposium on Signals, Circuits and Systems (ISSCS).

[4] W. Chu. Studying Aesthetics in Photographic Images Using a Computational Approach , 2013 .

[5] William Stafford Noble,et al. Support vector machine , 2013 .

[6] D. Berlyne. Conflict, arousal, and curiosity , 2014 .

[7] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.

[8] Vicente Ordonez,et al. High level describable attributes for predicting aesthetics and interestingness , 2011, CVPR 2011.

[9] Mats Sjöberg,et al. MediaEval 2017 Predicting Media Interestingness Task , 2016, MediaEval.

[10] Peter Salamon,et al. Can we measure beauty? Computational evaluation of coral reef aesthetics , 2015, PeerJ.

[11] Yan Ke,et al. The Design of High-Level Features for Photo Quality Assessment , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12] Luc Van Gool,et al. The Interestingness of Images , 2013, 2013 IEEE International Conference on Computer Vision.