Usage of mobile devices raises the need for organizing large personal multimedia collection. Their permanent availability and the ability to easily share retrieved pictures make this context propitious for building large collection of multimedia data. The present work focus on personal image collections acquired from mobile phones equipped with a camera. Our objective is to provide a classification of such collection in order to simplify the browsing task on a mobile device. We deal with the structuring of an image collection as a clustering problem. Our solution consists in building two distinct temporal and spatial partitions, based on the temporal and spatial metadata of each image. The main ingredients of our approach are the Gaussian mixture models, of which parameters are estimated with an adaptation of the EM algorithm, and the ICL criterion to determine the models complexities. First, we propose an incremental optimization algorithm to build non-hierarchical partitions in an automatic manner. It is then combined with an agglomerative algorithm to provide an incremental hierarchical algorithm, in order to summarize the collection. Finally, two techniques are proposed, combining the partitions obtained, to build hybrid spatio-temporal classifications taking into account the human machine interaction constraints.
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