Hybdrid Content Based Image Retrieval combining multi-objective interactive genetic algorithm and SVM

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche

Referenz

Romaric Pighetti; Denis Pallez; Frédéric Precioso: Hybdrid Content Based Image Retrieval combining multi-objective interactive genetic algorithm and SVM. Pattern Recognition (ICPR), 2012 21st International Conference on, 2849 - 2852.

DOI

Abstract

The amount of images contained in repositories or available on Internet has exploded over the last years. In order to retrieve efficiently one or several images in a database, the development of Content-Based Image Retrieval (CBIR) systems has become an intensively active research area. However, most proposed systems are keyword-based and few imply the end-user during the search (through relevance feedback). Visual low-level descriptors are then substituted to keywords but there is a gap between visual description and user expectations. We propose a new framework which combines a multi-objective interactive genetic algorithm, allowing a trade-off between image features and user evaluations, and a support vector machine to learn the user relevance feedback. We test our system on SIMPLIcity database, commonly used in the literature to evaluate CBIR systems using a genetic algorithm, and it outperforms the recent frameworks.

Extended Abstract

Bibtex

@INPROCEEDINGS{6460759,
author={R. Pighetti and D. Pallez and F. Precioso},
booktitle={Pattern Recognition (ICPR), 2012 21st International Conference on},
title={Hybdrid Content Based Image Retrieval combining multi-objective interactive genetic algorithm and SVM},
year={2012},
pages={2849-2852},
keywords={Internet;content-based retrieval;feature extraction;genetic algorithms;image retrieval;interactive systems;relevance feedback;support vector machines;visual databases;CBIR  systems;Internet;SIMPLIcity database;SVM;hybrid content-based image retrieval;image database;image features;image repository;keyword-based system;multiobjective interactive genetic algorithm;support vector machine;user evaluation;user expectations;user relevance feedback;visual description;visual low-level descriptors;Genetic algorithms;Image retrieval;Sociology;Support vector machines;Training;Vectors},
ISSN={1051-4651},
month={Nov},
url={http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6460759 http://de.evo-art.org/index.php?title=Hybdrid_Content_Based_Image_Retrieval_combining_multi-objective_interactive_genetic_algorithm_and_SVM },
}

Used References

M. Arevalillo-Herrez, F. J. Ferri, and S. Moreno-Picot: Distance-based relevance feedback using a hybrid interactive genetic algorithm for image retrieval. Applied Soft Computing, 11(2):1782-1791, 2011. http://dx.doi.org/10.1016/j.asoc.2010.05.022

E. Chang, S. Tong, K. Goh, and C. Chang: Support vector machine concept-dependent active learning for image retrieval. IEEE Trans. on Multimedia, 2, 2005. http://www.robotics.stanford.edu/~stong/papers/chang-tong-goh-chang-ieeemm.pdf

M. Crucianu, D. Estevez, V. Oria, and J.-P. Tarel: Hyperplane queries in a feature-space m-tree for speeding up active learning. In BDA, 23èmes Journées Bases de Données Avancées, BDA 2007, Marseille, 23-26 Octobre 2007, Actes (Informal Proceedings), 2007. https://www.researchgate.net/publication/221255864_Hyperplane_Queries_in_a_Feature-Space_M-tree_for_Speeding_up_Active_Learning http://perso.lcpc.fr/tarel.jean-philippe/publis/jpt-bda07.pdf

C. D. Ferreira, J. A. Santos, R. da S. Torres, M. A. Gonçalves, R. C. Rezende, and W. Fan: Relevance feedback based on genetic programming for image retrieval. Pattern Recogn. Lett., 32(1):27-37, jan 2011. http://dx.doi.org/10.1016/j.patrec.2010.05.015

S. Ganesh, K. Ramar, D. Manimegalai, and M. Sivakumar: Image retrieval using heuristic approach and genetic algorithm. Journal of Computational Information Systems, 8(4):1563-1571, 2012. http://www.jofcis.com/downloadpaper.aspx?id=1900&name=2012_8_4_1563_1571.pdf https://www.researchgate.net/publication/266416605_Image_Retrieval_Using_Heuristic_Approach_and_Genetic_Algorithm

D. Gorisse, M. Cord, and F. Precioso. Salsas: Sub-linear active learning strategy with approximate k-nn search. Pattern Recognition, 44(1011):2343-2357, 2011. http://dx.doi.org/10.1016/j.patcog.2010.12.009

P. Gosselin and M. Cord: Active learning methods for interactive image retrieval. Image Processing, IEEE Transactions on, 17(7):1200-1211, july 2008. http://dx.doi.org/10.1016/j.patcog.2010.12.009 https://hal.archives-ouvertes.fr/hal-00520292/document

S. C. Hoi, R. Jin, J. Zhu, and M. R. Lyu: Semi-supervised svm batch mode active learning for image retrieval. IEEE CVPR, 0:1-7, 2008. http://dx.doi.org/10.1109/CVPR.2008.4587350 http://www.cse.msu.edu/~rongjin/publications/cvpr-al-08.pdf

B. Kulis and K. Grauman. Kernelized locality-sensitive hashing for scalable image search. In IEEE ICCV, pages 2130-2137, 29 2009-oct. 2 2009. http://dx.doi.org/10.1109/ICCV.2009.5459466

C.-C. Lai and Y.-C. Chen. A user-oriented image retrieval system based on interactive genetic algorithm. Instrumentation and Measurement, IEEE Transactions on, 60(10):3318-3325, oct. 2011. http://dx.doi.org/10.1109/TIM.2011.2135010

N. Panda, K. Goh, and E. Y. Chang: Active learning in very large databases. MTAP, 31(3):249-267, 2006. http://dx.doi.org/10.1007/s11042-006-0043-1 http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.76.5446&rep=rep1&type=pdf

D. Parikh and K. Grauman: Relative attributes. In International Conference on Computer Vision (ICCV), 2011. http://dx.doi.org/10.1109/ICCV.2011.6126281 http://www.cs.utexas.edu/~grauman/papers/ParikhGrauman_ICCV2011_relative.pdf

M. Saadatmand-Tarzjan and H. Moghaddam: A novel evolutionary approach for optimizing content-based image indexing algorithms. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 37(1):139-153, feb. 2007. http://dx.doi.org/10.1109/TSMCB.2006.880137 http://profdoc.um.ac.ir/pubs_files/p11030308.pdf

H. Takagi. New iec research and frameworks. In Aspects of Soft Computing, Intelligent Robotics and Control, volume 241 of Studies in Computational Intelligence, pages 65-76. Springer, 2009. http://dx.doi.org/10.1007/978-3-642-03633-0_4

K. D. Tran: Content-based retrieval using a multiobjective genetic algorithm. In IEEE SoutheastCon, pages 561-569, april 2005. http://dx.doi.org/10.1109/SECON.2005.1423306

J. Wang, J. Li, and G. Wiederhold. Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE PAMI, 23(9):947-963, sep 2001. http://dx.doi.org/10.1109/34.955109

S.-F. Wang, X.-F. Wang, and J. Xue: An improved interactive genetic algorithm incorporating relevant feedback. In IEEE ICMLC, volume 5, pages 2996-3001, aug. 2005. http://dx.doi.org/10.1109/ICMLC.2005.1527456

Links

Full Text

internal file


Sonstige Links

http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6460759