Image retrieval using mixture models and EM algorithm

Authors:
Micheline Najjar;Christophe Ambroise;Jean Pierre Cocquerez
Affiliations:
Heudiasyc UMR CNRS, Universite de technologie de compiegne, Compiegne, France;Heudiasyc UMR CNRS, Universite de technologie de compiegne, Compiegne, France;Heudiasyc UMR CNRS, Universite de technologie de compiegne, Compiegne, France
Venue:
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Year:
2003

Citing 5
Cited 1

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
Similarity Measures

IEEE Transactions on Pattern Analysis and Machine Intelligence
ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases

Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Text Classification from Labeled and Unlabeled Documents using EM

Machine Learning - Special issue on information retrieval
Content-Based Image Retrieval at the End of the Early Years

IEEE Transactions on Pattern Analysis and Machine Intelligence

Fully automatic kidneys detection in 2d CT images: a statistical approach

MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an original system for interactive content-based image retrieval (CBIR). A novel approach for searching by similarity is introduced. It is based on a classification of the index database using mixture models and the EM algorithm. The presented retrieval system is evaluated and validated using a medical image database and the Washington University heterogeneous database (ANN).