Abstract: Personal photo album organization is a highly demanding domain where advanced tools are required to manage large photo collections. In contrast to many previous works, that try to solve the problem of organizing a single user photo sequence, we present a new technique to account for the concurrent photo sequence organization problem, that is the problem of organizing multiple photo sequences taken during the same event. Given a set of sequences acquired at the same place during the same temporal window by several users using different cameras, our framework is intended to capture the evolution of the event and groups photos based on temporal proximity and visual content. The method automatically organizes the reference sequence in a tree capturing the event structure. Such a structure is then used to align the remaining photo sequences to the reference one. We tested our approach on the publicly available Gallagher dataset and on a new dataset we collected; this new dataset is composed of four photo sequences taken by four users at a public event. Results demonstrate the effectiveness of our method.
Presti, L. L., & Cascia, M. L. (2012). Concurrent photo sequence organization. MultimediaTools and Applications,68(3), 777-803. doi:10.1007/s11042-012-1079-z
Abstract: In this paper we present a method to automatically segment a photo sequence in groups containing the same persons. Many methods in literature accomplish to this task by adopting clustering techniques. We model the problem as the search for probable associations between faces detected in subsequent photos considering the mutual exclusivity constraint: a person can not be in a photo two times, nor two faces in the same photo can be assigned to the same group. Associations have been found considering face and clothing descriptions. In particular, a two level architecture has been adopted: at the first level, associations are computed within meaningful temporal windows (situations); at the second level, the resulting clusters are re-processed to find associations across situations. Experiments confirm our technique generally outperforms clustering methods. We present an analysis of the results on a public dataset, enabling future comparison, and on private collections.
Presti, L. L., Morana, M., & Cascia, M. L. (2011). A data association approach to detect and organize people in personal photo collections. Multimedia Tools and Applications,61(2), 321-352. doi:10.1007/s11042-011-0839-5
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