@inproceedings{63b16e9f122c43438850fbaa376238ac,
title = "Thematic saliency detection using spatial-temporal context",
abstract = "We propose a new measurement of video saliency termed thematic video saliency. Video saliency is detected in terms of finding the thematic objects that frequently appear at the salient positions in the video scenes. By representing all image segments in the video as the spatial-temporal context, we build an affinity graph among them, and formulate the thematic object discovery as a novel cohesive sub-graph mining problem. A trust region algorithm is also proposed to solve the challenging optimization problem. Unlike individual image saliency or co-saliency analysis, our proposed video saliency fully incorporates the whole spatial-temporal video context. Experiments on our newly developed eye tracking dataset as well as other two datasets further validate the effectiveness of our method on video saliency detection.",
author = "Ye Luo and Gangqiang Zhao and Junsong Yuan",
year = "2013",
doi = "10.1109/ICCVW.2013.53",
language = "English",
isbn = "9781479930227",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "347--353",
booktitle = "Proceedings - 2013 IEEE International Conference on Computer Vision Workshops, ICCVW 2013",
address = "United States",
note = "14th IEEE International Conference on Computer Vision Workshops, ICCVW 2013 ; Conference date: 01-12-2013 Through 08-12-2013",
}