Photo Collection Organization

We have developed methods to:

 

Concurrent Photo Sequence Organization [Dataset available]

During a social event (a birthday party, a marriage, a trip…), it is pretty common each member of the social group takes is own pictures. The photo sequences collected by the members of the social group have interesting properties:

  • they are part of the same “story”: each member has captured a different “point of view” of the story;
  • they have been concurrently acquired but are not temporally synchronized;
  • they may be overlapping (in content) but may also be pretty different;
  • they have been acquired with different devices and settings.

We tackle an interesting problem: how to co-organize these pictures based on the underlying hidden story?

As all the sequences have been taken at the same social event, it is likely they would share a similar temporal-visual structure. Therefore, our goal is to learn the temporal and visual structure of the social event from a reference sequence, and then transfer it to the unprocessed sequences.

Please, for more details, refer to our work:

Lo Presti L., La Cascia M., “Concurrent Photo Sequence Organization”, Springer Journal on Multimedia Tools and Applications (MTAP), 2012, (pdf)

The Concurrent Photo Sequence Dataset is made available!! Take a look…

 

People Re-Identification in Personal Photo Collection

Personal Photo Collection Organization based on “who is in the picture?” is hard because:

  • face detection and recognition is “in the wild”, under varying pose and illumination conditions;
  • the number of identities in the collection is a priori unknown;
  • in case of “family” collection, recognition is harder!

We adopt tracking by detection methods to organize a photo collection taking advantage of the Mutual Exclusivity property of the pictures: an identity cannot appear twice in the same picture.

We have used a semi-supervised approach to learn on-line the appearance model of each identity. The decisions of the trained classifiers are fused by a probabilistic framework and used to self-train the appearance models (collaborative training).

Please, for more details, refer to
Lo Presti L., La Cascia M., “An On-line Learning Method for Face Association in Personal Photo Collection”, Elsevier Image and Vision Computing (IMAVIS), 2012, (pdf)

 

We have used a joint probabilistic data association approach to organize the faces detected in a photo sequence; the set of the most probable associations between detected faces and discovered identities is the maximum matching in a bipartite graph.

Please, for more details, refer to
Lo Presti L., Morana M., La Cascia M., “A data association approach to detect and organize people in personal photo collections”, Springer Journal on MTAP, 2012, (pdf)