Bayesian Probabilistic Models for Image Retrieval

Vassilios Stathopoulos, Joemon M. Jose
Proceedings of the Second Workshop on Applications of Pattern Analysis, PMLR 17:41-47, 2011.

Abstract

In this paper we present new probabilistic ranking functions for content based image retrieval. Our methodology generalises previous approaches and is based on the predictive densities of generative probabilistic models modelling the density of image features. We evaluate the proposed methodology and compare it against two state of the art image retrieval systems using a well known image collection.

Cite this Paper

BibTeX
@InProceedings{pmlr-v17-stathopoulos11a, title = {Bayesian Probabilistic Models for Image Retrieval}, author = {Stathopoulos, Vassilios and Jose, Joemon M.}, booktitle = {Proceedings of the Second Workshop on Applications of Pattern Analysis}, pages = {41--47}, year = {2011}, editor = {Diethe, Tom and Balcazar, Jose and Shawe-Taylor, John and Tirnauca, Cristina}, volume = {17}, series = {Proceedings of Machine Learning Research}, address = {CIEM, Castro Urdiales, Spain}, month = {19--21 Oct}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v17/stathopoulos11a/stathopoulos11a.pdf}, url = {https://proceedings.mlr.press/v17/stathopoulos11a.html}, abstract = {In this paper we present new probabilistic ranking functions for content based image retrieval. Our methodology generalises previous approaches and is based on the predictive densities of generative probabilistic models modelling the density of image features. We evaluate the proposed methodology and compare it against two state of the art image retrieval systems using a well known image collection.} }
Endnote
%0 Conference Paper %T Bayesian Probabilistic Models for Image Retrieval %A Vassilios Stathopoulos %A Joemon M. Jose %B Proceedings of the Second Workshop on Applications of Pattern Analysis %C Proceedings of Machine Learning Research %D 2011 %E Tom Diethe %E Jose Balcazar %E John Shawe-Taylor %E Cristina Tirnauca %F pmlr-v17-stathopoulos11a %I PMLR %P 41--47 %U https://proceedings.mlr.press/v17/stathopoulos11a.html %V 17 %X In this paper we present new probabilistic ranking functions for content based image retrieval. Our methodology generalises previous approaches and is based on the predictive densities of generative probabilistic models modelling the density of image features. We evaluate the proposed methodology and compare it against two state of the art image retrieval systems using a well known image collection.
RIS
TY - CPAPER TI - Bayesian Probabilistic Models for Image Retrieval AU - Vassilios Stathopoulos AU - Joemon M. Jose BT - Proceedings of the Second Workshop on Applications of Pattern Analysis DA - 2011/10/21 ED - Tom Diethe ED - Jose Balcazar ED - John Shawe-Taylor ED - Cristina Tirnauca ID - pmlr-v17-stathopoulos11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 17 SP - 41 EP - 47 L1 - http://proceedings.mlr.press/v17/stathopoulos11a/stathopoulos11a.pdf UR - https://proceedings.mlr.press/v17/stathopoulos11a.html AB - In this paper we present new probabilistic ranking functions for content based image retrieval. Our methodology generalises previous approaches and is based on the predictive densities of generative probabilistic models modelling the density of image features. We evaluate the proposed methodology and compare it against two state of the art image retrieval systems using a well known image collection. ER -
APA
Stathopoulos, V. & Jose, J.M.. (2011). Bayesian Probabilistic Models for Image Retrieval. Proceedings of the Second Workshop on Applications of Pattern Analysis, in Proceedings of Machine Learning Research 17:41-47 Available from https://proceedings.mlr.press/v17/stathopoulos11a.html.

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