Neural Computation
Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Name-It: Naming and Detecting Faces in News Videos
IEEE MultiMedia
Weakly Supervised Learning of Visual Models and Its Application to Content-Based Retrieval
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
On scaling latent semantic indexing for large peer-to-peer systems
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Slightly Supervised Learning of Part-Based Appearance Models
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 6 - Volume 06
IEEE Transactions on Image Processing
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This work deals with estimation of a probability density, which is a common issue in multimedia pattern recognition. The originality comes from its computation in a distributed manner, since the study is motivated by the perspective of a multimedia indexing and retrieval peer-to-peer system over the internet. In a decentralized fashion, algorithms and data from various contributors would cooperate towards a collective statistical learning.In this setting, aggregation of probabilistic Gaussian mixture models of the same class, but estimated on several nodes on different data sets, is a typical need, which we address herein. The proposed approach for fusion only requires moderate computation at each node and little data to transit between nodes. Both properties are obtained by aggregating models via their (few) parameters, rather than via multimedia data itself. Mixture models are in fact concatenated, then reduced to a suitable number of Gaussian components. A modification on Kullback divergence leads to an iterative scheme for estimating this aggregated model. We provide experimental results on a speaker recognition task with real data, in a gossip propagation setting.