Photo Stream Alignment and Summarization for Collaborative Photo Collection and Sharing

With the popularity of digital cameras and camera phones, it is common for different people, who may or may not know each other, to attend the same event and take pictures and videos from different spatial or personal perspectives. Within the realm of social media, it is desirable to enable these people to select and share their pictures and videos in order to enrich memories and facilitate social networking. However, it is cumbersome to manually manage these photos from different cameras, of which the clocks settings are often not calibrated. In this paper, we propose automatic algorithms to address the above problems. First, we accurately align different photo streams or sequences from different photographers for the same event in chronological order on a common timeline, while respecting the time constraints within each photo stream. Given the preferred similarity measures (e.g., visual, and spatial similarities), our algorithm performs photo stream alignment via matching on a bipartite kernel sparse representation graph that forces the data connections to be sparse in an explicit fashion. Furthermore, we can produce a summary master stream from the aligned super stream of photos for efficient sharing by removing those redundant photos in the super stream while accounting for the temporal integrity. Based on a similar kernel sparse representation graph, our master stream summarization algorithm performs greedy backward selection to drop redundant photos without affecting the integrity of remaining photos for the entire event. We evaluate our algorithms on real-world personal online albums for 36 events and demonstrate its efficacy in automatically facilitating collaborative photo collection and sharing.