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Article

Quantization-based probabilistic feature modeling for kernel design in content-based image retrieval

Authors:

Antonio OrtegaAuthors Info & Claims

MIR '06: Proceedings of the 8th ACM international workshop on Multimedia information retrieval

Pages 23 - 32

https://doi.org/10.1145/1178677.1178684

Published: 26 October 2006 Publication History

Abstract

In this paper, a quantization-based probabilistic feature modeling approach is proposed for relevance feedback in content-based image retrieval. We demonstrate its performance by using the resulting models within a support vector machine (SVM) based technique. Each feature component is quantized and mapped to probabilistic quantities representing the likelihood of the image being relevant (and irrelevant). These probabilistic quantities are then used to derive an information divergence-based kernel function for SVM classification which we introduced in earlier work. We show that the proposed method leads to the optimal maximum likelihood solution as the knowledge of the actual underlying probability model improves (i.e.,as the feature space is partitioned into arbitrarily small "regions "and accurate models are known for all regions). vWe investigate several practical quantization designs for feature modeling specifically in relevance feedback applications,where the scarcity of the data and high dimensionality prevent usage of vector quantization and parametric modeling approaches.Our proposed framework naturally takes into account the statistics of the data that is available during relevance feedback for the purpose of discriminating between relevant and irrelevant images.Experiments with the Corel dataset show that quantizers specifically designed for this application achieve gains over simple uniform quantizers (e.g.,5% to 10% in retrieval accuracy) when combined with our information divergence kernel. This kernel achieves gains (e.g.,17% in retrieval accuracy after first relevance feedback)as compared to the standard radial basis function (RBF) kernel used for SVM-based relevance feedback.

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Cited By

(2016)On interactive learning-to-rank for IRNeurocomputing10.1016/j.neucom.2016.03.084208:C(3-24)Online publication date: 5-Oct-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.03.084
Thomee BLew M(2012)Interactive search in image retrieval: a surveyInternational Journal of Multimedia Information Retrieval10.1007/s13735-012-0014-41:2(71-86)Online publication date: 8-Jun-2012
https://doi.org/10.1007/s13735-012-0014-4
Calderero FMarques FOrtega A(2009)Performance evaluation of probability density estimators for unsupervised information theoretical region mergingProceedings of the 16th IEEE international conference on Image processing10.5555/1819298.1819933(4337-4340)Online publication date: 7-Nov-2009
https://dl.acm.org/doi/10.5555/1819298.1819933
Show More Cited By

Index Terms

Quantization-based probabilistic feature modeling for kernel design in content-based image retrieval
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment

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Published In

cover image ACM Conferences

MIR '06: Proceedings of the 8th ACM international workshop on Multimedia information retrieval

October 2006

344 pages

ISBN:1595934952

DOI:10.1145/1178677

General Chairs:
James Z. Wang
The Pennsylvania State University
,
Nozha Boujemaa
INRIA Rocquencourt, France
,
Yixin Chen
The University of Mississippi

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 October 2006

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MM06: The 14th ACM International Conference on Multimedia 2006

October 26 - 27, 2006

California, Santa Barbara, USA

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Cited By

(2016)On interactive learning-to-rank for IRNeurocomputing10.1016/j.neucom.2016.03.084208:C(3-24)Online publication date: 5-Oct-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.03.084
Thomee BLew M(2012)Interactive search in image retrieval: a surveyInternational Journal of Multimedia Information Retrieval10.1007/s13735-012-0014-41:2(71-86)Online publication date: 8-Jun-2012
https://doi.org/10.1007/s13735-012-0014-4
Calderero FMarques FOrtega A(2009)Performance evaluation of probability density estimators for unsupervised information theoretical region mergingProceedings of the 16th IEEE international conference on Image processing10.5555/1819298.1819933(4337-4340)Online publication date: 7-Nov-2009
https://dl.acm.org/doi/10.5555/1819298.1819933
Calderero FMarques FOrtega A(2009)Performance evaluation of probability density estimators for unsupervised information theoretical region merging2009 16th IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2009.5413621(4397-4400)Online publication date: Nov-2009
https://doi.org/10.1109/ICIP.2009.5413621
Wang JBoujemaa NChen Y(2007)High diversity transforms multimedia information retrieval into a cross-cutting fieldACM SIGMOD Record10.1145/1276301.127631536:1(57-59)Online publication date: 1-Mar-2007
https://dl.acm.org/doi/10.1145/1276301.1276315

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