Building and Tracking Hierarchical Geographical & Temporal Partitions for Image Collection Management on Mobile Devices

International audienceUsage of mobile devices (phones, digital cameras) raises the need for organizing large personal image collections. In accordance with studies on user needs, we propose a statistical criterion and an associated optimization technique, relying on geo-temporal image metadata, for building and tracking a hierarchical structure on the image collection. In a mixture model framework, particularities of the application and typical data sets are taken into account in the design of the scheme (incrementality, ability to cope with non-Gaussian data, with both small and large samples). Results are reported on real data sets

Building and Tracking Hierarchical Geographical & Temporal Partitions for Image Collection Management on Mobile Devices

International audienceUsage of mobile devices (phones, digital cameras) raises the need for organizing large personal image collections. In accordance with studies on user needs, we propose a statistical criterion and an associated optimization technique, relying on geo-temporal image metadata, for building and tracking a hierarchical structure on the image collection. In a mixture model framework, particularities of the application and typical data sets are taken into account in the design of the scheme (incrementality, ability to cope with non-Gaussian data, with both small and large samples). Results are reported on real data sets

Geo-temporal structuring of a personal image database with two-level variational-Bayes mixture estimation

International audienceThis paper addresses unsupervised hierarchical classication of personal documents tagged with time and geolocation stamps. The target application is browsing among these documents. A rst partition of the data is built, based on geo-temporal measurement. The events found are then grouped according to geolocation. This is carried out through tting a two-level hierarchy of mixture models to the data. Both mixtures are estimated in a Bayesian setting, with a variational proce- dure: the classical VBEM algorithm is applied for the ner level, while a new variational-Bayes-EM algorithm is introduced to search for suitable groups of mixture components from the ner level. Experimental results are reported on articial and real data

Presentation of multiple GEO-referenced videos

Master'sMASTER OF SCIENC

The Transmission and Processing of Sensor-rich Videos in Mobile Environment

Ph.DDOCTOR OF PHILOSOPH