  We live in a society where the large majority of the population has a
camera-equipped smartphone. In addition, hard drives and cloud storage are
getting cheaper and cheaper, leading to a tremendous growth in stored personal
photos. Unlike photo collections captured by a digital camera, which typically
are pre-processed by the user who organizes them into event-related folders,
smartphone pictures are automatically stored in the cloud. As a consequence,
photo collections captured by a smartphone are highly unstructured and because
smartphones are ubiquitous, they present a larger variability compared to
pictures captured by a digital camera. To solve the need of organizing large
smartphone photo collections automatically, we propose here a new methodology
for hierarchical photo organization into topics and topic-related categories.
Our approach successfully estimates latent topics in the pictures by applying
probabilistic Latent Semantic Analysis, and automatically assigns a name to
each topic by relying on a lexical database. Topic-related categories are then
estimated by using a set of topic-specific Convolutional Neuronal Networks. To
validate our approach, we ensemble and make public a large dataset of more than
8,000 smartphone pictures from 10 persons. Experimental results demonstrate
better user satisfaction with respect to state of the art solutions in terms of
organization.
