investigating keyframe selection methods in the novel domain of passively captured visual lifelogs
The SenseCam is a passive capture wearable camera and when worn continuously it takes an average of 1,900 images per day. It can be used to create a personal lifelog or visual recording of a wearer's life which can be helpful as an aid to human memory. For such a large amount of visual information to be useful, it needs to be structured into "events", which can be achieved through automatic segmentation. An important component of this structuring process is the selection of keyframes to represent individual events. This work investigates a variety of techniques for the selection of a single representative keyframe image from each event, in order to provide the user with an instant visual summary of that event. In our experiments we use a large test set of 2,232 lifelog events collected by 5 users over a time period of one month each. We propose a novel keyframe selection technique which seeks to select the image with the highest "quality" as the keyframe. The inclusion of "quality" approaches in keyframe selection is demonstrated to be useful owing to the high variability in image visual quality within passively captured image collections.
keyframe selection visual lifelogs image quality metrics