Recognizing human activity in still images by integrating group-based contextual cues

Images with wider angles usually capture more persons in wider scenes, and recognizing individuals' activities in these images based on existing contextual cues usually meet difficulties. We instead construct a novel group-based cue to utilize the context carried by suitable surrounding persons. We propose a global-local cue integration model (GLCIM) to find a suitable group of local cues extracted from individuals and form a corresponding global cue. A fusion restricted Boltzmann machine, a focal subspace measurement and a cue integration algorithm based on entropy are proposed to enable the GLCIM to integrate most of the relevant local cues and least of the irrelevant ones into the group. Our experiments demonstrate how integrating group-based cues improves the activity recognition accuracies in detail and show that all of the key parts of GLCIM make positive contributions to the increases of the accuracies.