Keyframe detection in visual lifelogs

Abstract

The SenseCam is a wearable camera that passively captures images. Therefore, it requires no conscious effort by a user in taking a photo. A Visual Diary from such a source could prove to be a valuable tool in assisting the elderly, individuals with neurodegenerative diseases, or other traumas. One issue with Visual Lifelogs is the large volume of image data generated. In previous work, we segmented a day's worth of images into more manageable segments, i.e. into distinct events or activities. However, each event could still consist of 80-100 images, thus, in this paper we propose a novel approach to selecting the key images within an event using a combination of MPEG-7 and Scale Invariant Feature Transform (SIFT) features. Copyright 2008 ACM.