A New Clustering Methodology for Group Photos Taken by Multiple Travelers

Abstract

According to the popular use of digital camera, a traveler group carries multiple cameras for the same event. Previous studies on digital photo focused on massive unrelated photos or collections of a private user. But group travelers can carry several cameras and take photos simultaneously at the same time. Thus, an effective management for group photos is main issue for a traveler group. Since group photos from different photographers share their content, people need to collate the photos and classify them. We propose several supervised and unsupervised clustering methods for group photos. Previous studies are not applicable to group photos, because group photos do not guarantee clear relevance between photos which shown in private photo album. The proposed supervised clustering method, using spatio-temporal similarity, obtains a true cluster set of a specific camera from a user. It extracts discriminating features from given clusters and applies them to cluster other photos. Unsupervised methods use temporal photo blocks to compensate spatial variation of photos from a user. We use hierarchical clustering and neural network based clustering. In experiments, we show clustering results from real nomadic photo data. People can use a method suited to their needs.

User Experience Study of Multiple Photo Streams Visualization

“…In (Patel, Clawson et al 2009), a collocated-synchronous mobile application for photo displaying and sharing has been implemented. In addition, (Jang, Yoon et al 2009;Yang, Luo et al 2011) have proposed novel techniques in order to organizing multiple photo streams for collocated events. However, these approaches just covered collocated events and did not propose intuitive visualizations of photos that were obtained from different sources.…”