Temporally Coherent Interpretations for Long Videos Using Pattern Theory

Fillipe Souza, Sudeep Sarkar, Anuj Srivastava, Jingyong Su; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1229-1237


Graph-theoretical methods have successfully provided semantic and structural interpretations of images and videos. A recent paper introduced a pattern-theoretic approach that allows construction of flexible graphs for representing interactions of actors with objects and inference is accomplished by an efficient annealing algorithm. Actions and objects are termed generators and their interactions are termed bonds; together they form high-probability configurations, or interpretations, of observed scenes. This work and other structural methods have generally been limited to analyzing short videos involving isolated actions. Here we provide an extension that uses additional temporal bonds across individual actions to enable semantic interpretations of longer videos. Longer temporal connections improve scene interpretations as they help discard (temporally) local solutions in favor of globally superior ones. Using this extension, we demonstrate improvements in understanding longer videos, compared to individual interpretations of non-overlapping time segments. We verified the success of our approach by generating interpretations for more than 700 video segments from the YouCook data set, with intricate videos that exhibit cluttered background, scenarios of occlusion, viewpoint variations and changing conditions of illumination. Interpretations for long video segments were able to yield performance increases of about 70 and, in addition, proved to be more robust to different severe scenarios of classification errors.

Related Material

author = {Souza, Fillipe and Sarkar, Sudeep and Srivastava, Anuj and Su, Jingyong},
title = {Temporally Coherent Interpretations for Long Videos Using Pattern Theory},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}