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A Statistical Framework for Mental Targets Search Using Mixture Models

  • Taoufik BdiriEmail author
  • Nizar Bouguila
  • Djemel Ziou
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 607)

Abstract

Image retrieval is usually based on specific user needs that are expressed under the form of explicit queries that lead to retrieve target images. In many cases, a given user does not possess the adequate tools and semantics to express what he/she is looking for, thus, his/her target image resides in his/her mind while he/she can visually identify it. We propose in this work, a statistical framework that enables users to start a search process and interact with the system in order to find their target “mental image”, using visual features only. Our bayesian formulation provides the possibility of searching multi target classes within the same search process. Data are modeled by a generalized inverted Dirichlet mixture that also serves to quantify the similarities between images. We run experiments including real users and we present a case study of a search process that gives promising results in terms of number of iterations needed to find the mental target classes within a given dataset.

Keywords

Mental search Image retrieval Bayesian models Generalized inverted dirichlet Mixture models 

Notes

Acknowledgments

The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada
  2. 2.Concordia Institute for Information Systems EngineeringConcordia UniversityMontrealCanada
  3. 3.DI, Faculté des SciencesUniversité de SherbrookeSherbrookeCanada

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