@Article{StefanLonn2019,
author="Stefan Lonn
and Petia Radeva
and Mariella Dimiccoli",
title="Smartphone picture organization: A hierarchical approach",
journal="Computer Vision and Image Understanding",
year="2019",
volume="187",
pages="102789",
abstract="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 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.",
optnote="MILAB; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3297), last updated on Tue, 25 Jan 2022 12:02:14 +0100",
opturl="https://doi.org/10.1016/j.cviu.2019.07.009",
file=":http://refbase.cvc.uab.es/files/LRD2019.pdf:PDF"
}