Actionness Ranking with Lattice Conditional Ordinal Random Fields

Wei Chen, Caiming Xiong, Ran Xu, Jason J. Corso; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 748-755

Abstract


Action analysis in image and video has been attracting more and more attention in computer vision. Recognizing specific actions in video clips has been the main focus. We move in a new, more general direction in this paper and ask the critical fundamental question: what is action, how is action different from motion, and in a given image or video where is the action? We study the philosophical and visual characteristics of action, which lead us to define actionness: intentional bodily movement of biological agents (people, animals). To solve the general problem, we propose the lattice conditional ordinal random field model that incorporates local evidence as well as neighboring order agreement. We implement the new model in the continuous domain and apply it to scoring actionness in both image and video datasets. Our experiments demonstrate not only that our new model can outperform the popular ranking SVM but also that indeed action is distinct from motion.

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[bibtex]
@InProceedings{Chen_2014_CVPR,
author = {Chen, Wei and Xiong, Caiming and Xu, Ran and Corso, Jason J.},
title = {Actionness Ranking with Lattice Conditional Ordinal Random Fields},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}