Incremental and hierarchical classification of a personal image collection on mobile devices

International audienceBrowsing multimedia collection on mobile devices raises the needs for new multimedia indexing solutions. In this paper, we focus on the management of personal image collections. We propose a method to simplify the browsing task on such a collection. The contributions reside in an incremental hierarchical algorithm, a method to provide a textual representation of the groups obtained and an algorithm to build a geo-temporal view of the collection. The proposed incremental hierarchical algorithm builds a temporal tree from the time stamp of each image. We opt here for a combination of a supervised clustering and an incremental algorithm based on mixture model. Good properties of the hierarchy are determined automatically thanks to the Integrated Likelihood Criterion (ICL). Based on the events obtained, a textual representation is proposed and then used to improve our temporal classification, combining geographical and temporal information. Results are validated on several real user collections with our prototype MyOwnLife

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

A decentralized and robust approach to estimating a probabilistic mixture model for structuring distributed data

International audienceData sharing services on the web host huge amounts of resources supplied and accessed by millions of users around the world. While the classical approach is a central control over the data set, even if this data set is distributed, there is growing interesting in decentralized solutions, because of good properties (in particularity, privacy and scaling up). In this paper, we explore a machine learning side of this work direction. We propose a novel technique for decentralized estimation of probabilistic mixture models, which are among the most versatile generative models for understanding data sets. More precisely, we demonstrate how to estimate a global mixture model from a set of local models. Our approach accommodates dynamic topology and data sources and is statistically robust, i.e. resilient to the presence of unreliable local models. Such outlier models may arise from local data which are outliers, compared to the global trend, or poor mixture estimation. We report experiments on synthetic data and real geo-location data from Flickr

Fast aggregation of Student mixture models

International audienceThis paper deals with probabilistic models, that take the form of mixtures of Student distributions. Student distributions are known to be more statistically robust than Gaussian distributions, with regard to outliers (i.e. data that cannot be reasonnably explained by any component in the mixture and that do not justifiy an extra component. Our contribution is as follows : we show how several mixtures of Student distributions may be agregated into a single mixture, without resorting to sampling. The trick is that, as is well known, a Student distribution may be expressed as an infinite mixture of Gaussians, where the variances follow a Gamma distribution

Organisation statistique spatio-temporelle d'une collection d'images acquises d'un terminal mobile géolocalisé

International audienceNous présentons une technique automatique d'organisation de collection d'images personnelles, pour répondre aux besoins particuliers émergents des téléphones portables équipés d'appareil photographique. Après avoir examiné ce qui fait la particularit é de ce contexte, nous proposons une technique de structuration de collection d'image basée sur la date et le lieu de prise de vue des images. L'objectif est formalisé comme un problème de classification non-supervisée, temporelle et spatiale. Le critère statistique de vraisemblance complétée intégrée (ICL) est retenu, car il fournit une solution efficace pour déterminer la complexité du modèle et un bon niveau de séparabilité de ses composantes, tout en limitant le caractère arbitraire de la paramétrisation. La fiabilité des classifications obtenues est ensuite évaluée, afin d'en sélectionner la plus pertinente, pour fournir une structure utilisable avec une interface de type calendrier électronique permettant d'explorer la collection

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

Fast aggregation of Student mixture models

International audienceThis paper deals with probabilistic models, that take the form of mixtures of Student distributions. Student distributions are known to be more statistically robust than Gaussian distributions, with regard to outliers (i.e. data that cannot be reasonnably explained by any component in the mixture and that do not justifiy an extra component. Our contribution is as follows : we show how several mixtures of Student distributions may be agregated into a single mixture, without resorting to sampling. The trick is that, as is well known, a Student distribution may be expressed as an infinite mixture of Gaussians, where the variances follow a Gamma distribution

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

Cavity nano-optomechanics in the ultrastrong coupling regime with ultrasensitive force sensors

In a canonical optomechanical system, mechanical vibrations are dynamically encoded on an optical probe field which reciprocally exerts a backaction force. Due to the weak single photon coupling strength achieved with macroscopic oscillators, most of existing experiments were conducted with large photon numbers to achieve sizeable effects, thereby causing a dilution of the original optomechanical non-linearity. Here, we investigate the optomechanical interaction of an ultrasensi-tive suspended nanowire inserted in a fiber-based microcavity mode. This implementation allows to enter far into the hitherto unexplored ultrastrong optomechanical coupling regime, where one single intracavity photon can displace the oscillator by more than its zero point fluctuations. To fully characterize our system, we implement nanowire-based scanning probe measurements to map the vectorial optomechanical coupling strength, but also to reveal the intracavity optomechanical force field experienced by the nanowire. This work establishes that the single photon cavity optomechanics regime is within experimental reach. Introduction-The field of optomechanics has gone through many impressive developments over the last decades [1]. The coupling between a probe light field and a mechanical degree of freedom, an oscillator, possibly assisted by a high finesse cavity was early proposed as an ideal platform to explore the quantum limits of ultrasen-sitive measurements, where the quantum fluctuations of the light are the dominant source of measurement noise [2-5]. The measurement backaction was also employed to manipulate the oscillator state through optical forces and dynamical backaction, leading to optomechanical correlations between both components of the system. In this framework, ground state cooling, mechanical detection of radiation pressure quantum noise, advanced correlation between light and mechanical states or optomechanical squeezing were reported [6-19]. All those impressive results were obtained in the linear regime of cavity optomechanics, making use of large photon numbers, where the interaction Hamiltonian is linearized around an operating setpoint. However, the optomechanical interaction possesses an intrinsic non-linearity at the single excitation level, which has for the moment remained far from experimental reach due to the weak single photon coupling strength achieved with macroscopic oscillators. This regime is achieved when a single photon in the cavity shifts the static rest position of the mechanical resonator by a quantity δx (1) which is larger than its zero point fluctuations δx zpf. A very strong optomechanical interaction is indeed needed to fulfil this condition since it requires g 0 /Ω m > 1 where g 0 is the single photon optomechanical coupling and Ω m the resonant pulsation of the mechanical oscillator. Operating in the ultra-strong coupling regime is thus an experimenta