A personal collection of photos shows large variability in the depicted items, making difficult a fully automated solution to cope with sensory and semantic gaps. Emotions and non-visual contextual information can be very important to address those problems. Manual annotations are key, but their time-consuming nature alienate users from doing them. One solution is to lower the annotation effort, building solutions on top of algorithms that prepare a context separation, making possible the reuse of annotations. In this paper we present a segmentation algorithm that uses spatio-temporal information to segment personal photo collections. The algorithm is assessed in a user study, using the par- ticipants own photos. The results show users make none or few changes to the proposed segmentations, indicating an acceptance of the algorithm outcome.
|Journal||Multimedia Tools and Applications Journal|
|Publication status||Published - Mar 2017|
- Empirical user study
- Segmentation algorithm
- Personal photo collections