Feature selection based on genetic algorithm for CBIR

Aus de_evolutionary_art_org
Version vom 22. Juni 2016, 22:39 Uhr von Gubachelier (Diskussion | Beiträge) (Die Seite wurde neu angelegt: „ == Referenz == T. Zhao, J. Lu, Y. Zhang, Q. Xiao: Feature selection based on genetic algorithm for CBIR. IEEE Congress on Image and Signal Processing, 2…“)

(Unterschied) ← Nächstältere Version | Aktuelle Version (Unterschied) | Nächstjüngere Version → (Unterschied)
Wechseln zu: Navigation, Suche


Referenz

T. Zhao, J. Lu, Y. Zhang, Q. Xiao: Feature selection based on genetic algorithm for CBIR. IEEE Congress on Image and Signal Processing, 2, 2008, pp. 495–499.

DOI

http://dx.doi.org/10.1109/CISP.2008.90

Abstract

Automated techniques to optimize feature descriptor weights and select optimum feature descriptor subset are desirable as a way to enhance the performance of content based image retrieval system. In our system, all the MPEG-7 image feature descriptors including color descriptors, texture descriptors and shape descriptors are used to represent low-level image features. We use a real coded chromosome genetic algorithm (GA) and k-nearest neighbor (k-NN) classification accuracy as fitness function to optimize weights. Meanwhile, a binary one and k-NN classification accuracy combining with the size of feature descriptor subset as fitness function are used to select optimum feature descriptor subset. Furthermore, we propose two kinds of two-stage feature selection schemes for weight optimization and descriptor subset selection, which are the integration of a real coded GA and a binary one. The experimental results over 2000 classified Corel images show that with weight optimization, the accuracy of image retrieval system is improved; with the selection of optimum feature descriptor subset, both the accuracy and the efficiency are improved.

Extended Abstract

Bibtex

@INPROCEEDINGS{4566353,
author={T. Zhao and J. Lu and Y. Zhang and Q. Xiao},
booktitle={Image and Signal Processing, 2008. CISP '08. Congress on},
title={Feature Selection Based on Genetic Algorithm for CBIR},
year={2008},
volume={2},
pages={495-499},
keywords={Automation;Biological cells;Content based retrieval;Feature extraction;Genetic algorithms;Image retrieval;MPEG 7 Standard;Multimedia databases;Programmable logic arrays;Shape measurement;Feature selection;genetic algorithm;image retrieval;k-nearest neighbor classifier;multimedia content description interface},

doi={10.1109/CISP.2008.90},

url={http://dx.doi.org/10.1109/CISP.2008.90 http://de.evo-art.org/index.php?title=Feature_selection_based_on_genetic_algorithm_for_CBIR },
month={May},
}

Used References

R. Datta, D. Joshi, and J. Wang, "Image retrieval: Ideas, influences, and trends of the new age", ACM transactions on computing surveys, 2007.

S.T. Li, J.S. Taylor, "Comparison and fusion of multiresolution features for texture classification", Pattern Recognition Letters, 2005, 26, 633-638. http://dx.doi.org/10.1016/j.patrec.2004.09.013

B.S. Manjunath, J.R. Ohm, V.V. Vasudevan, and et al, "Color and texture descriptors", IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11, 703-715. http://dx.doi.org/10.1109/76.927424

R. Manthalkar, P.K. Biswas, and B.N Chatterji, "Rotation and scale invariant texture features using discrete wavelet packet transform", Pattern Recognition Letters, 2003, 24 (14), 2455-2462. http://dx.doi.org/10.1016/S0167-8655(03)00090-4

ISO/IEC/JTC1/SC29/WG11/N4062. 2001b, "Text of ISO/IEC 15938-3 multimedia content description interface part 3: visual", ISO/IEC, 2001.

H. Shao, J.W. Zhang, W.C. Cui, and et al, "Automatic feature weight assignment based on genetic algorithm for image retrieval", Proceedings of the 2003 IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, China, 2003, 731-735. http://dx.doi.org/10.1109/RISSP.2003.1285675

M.E. Celebi, YA. Aslandogan, "Content-based image retrieval incorporating models of human perception", Proceedings of the International Conference on Information Technology: Coding and Computing, 2004, 241-245. http://dx.doi.org/10.1109/ITCC.2004.1286639

W. Jiang, G.H. Er, Q.H. Dai, and et al, "Similarity-based online feature selection in content-based image retrieval", Transactions on Image Processing, 2006, 15(3), 702-712. http://dx.doi.org/10.1109/TIP.2005.863105

B. Hernández, G. Olague, R. Hammoud, and et al, "Visual learning of texture descriptors for facial expression recognition in thermal imagery", Computer Vision and Image Understanding, 2007, 106(2-3): 258-269. http://dx.doi.org/10.1016/j.cviu.2006.08.012

M. Bober, "MPEG-7 visual shape descriptor", IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11, 716-719. http://dx.doi.org/10.1109/76.927426

ISO/IEC/JTC1/SC29/WG11, Doc. N4063, "MPEG-7 visual experimentation model (XM), Version 10.0", ISO/IEC, Mar. 2001.

J.C. Felipe, J.B. Olioti, A.J.M. Traina, and et al, "A low-cost approach for effective shape-based retrieval and classification of medical images", Proceedings of the Seventh IEEE International Symposium on Multimedia, 2005. http://dx.doi.org/10.1109/ISM.2005.12

B. Bhanu, Y.Q. Lin, "Genetic algorithm based feature selection for target detection in SAR images", Image and Vision Computing, 2003, 21(7), 591-608. http://dx.doi.org/10.1016/S0262-8856(03)00057-X

I.S. Oh, J.S. Lee, and B.R. Moon, "Hybrid genetic algorithms for feature selection", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(11), 1424-1437. http://dx.doi.org/10.1109/TPAMI.2004.105

G. Das, S. Ray, and C. Wilson, "Feature re-weighting in content-based image retrieval", Proceedings of International Conference on Image and Video Retrieval, 2006, 193-200. http://dx.doi.org/10.1109/TPAMI.2004.105

Links

Full Text

internal file


Sonstige Links