Feature selection based-on genetic algorithm for image annotation

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


J. Lu, T. Zhao, Y. Zhang: Feature selection based-on genetic algorithm for image annotation. Knowledge-Based Systems 21 (2008) 887–891.




Machine learning techniques for feature selection, which include the optimization of feature descriptor weights and the selection of optimal feature descriptor subset, are desirable to enhance the performance of image annotation systems. In our system, the multimedia content description interface (MPEG-7) image feature descriptors consisting of color descriptors, texture descriptors and shape descriptors are employed to represent low-level image features. We use a real coded chromosome genetic algorithm and k-nearest neighbor (k-NN) classification accuracy as fitness function to optimize the weights of MPEG-7 image feature descriptors. A binary one and k-NN classification accuracy combining with the size of feature descriptor subset as fitness function are used to select optimal MPEG-7 feature descriptor subset. Furthermore, a bi-coded chromosome genetic algorithm is used for the simultaneity of weight optimization and descriptor subset selection, whose fitness function is the same as that of the binary one. The experimental results over 2000 classified Corel images show that with the real coded genetic algorithm, the binary coded one and the bi-coded one, the accuracies of image annotation system are improved by 7%, 9% and 13.6%, respectively, comparing to the method without machine learning. Furthermore, 2 of 25 MPEG-7 feature descriptors are selected with the binary coded genetic algorithm and four with the bi-coded one, which may improve the efficiency of system significantly.

Extended Abstract


author = {Lu, Jianjiang and Zhao, Tianzhong and Zhang, Yafei},
title = {Feature Selection Based-on Genetic Algorithm for Image Annotation},
journal = {Know.-Based Syst.},
issue_date = {December, 2008},
volume = {21},
number = {8},
month = dec,
year = {2008},
issn = {0950-7051},
pages = {887--891},
numpages = {5},
url = {http://dx.doi.org/10.1016/j.knosys.2008.03.051 http://de.evo-art.org/index.php?title=Feature_selection_based-on_genetic_algorithm_for_image_annotation },
doi = {10.1016/j.knosys.2008.03.051},
acmid = {1457095},
publisher = {Elsevier Science Publishers B. V.},
address = {Amsterdam, The Netherlands, The Netherlands},
keywords = {Feature selection, Genetic algorithm, Image annotation, Multimedia content description interface, k-Nearest neighbor classifier},


Used References

[] Ying Liu , Dengsheng Zhang , Guojun Lu , Wei-Ying Ma, A survey of content-based image retrieval with high-level semantics, Pattern Recognition, v.40 n.1, p.262-282, January, 2007 http://dx.doi.org/10.1016/j.patcog.2006.04.045

[2] D. Djordjevic , E. Izquierdo, An Object- and User-Driven System for Semantic-Based Image Annotation and Retrieval, IEEE Transactions on Circuits and Systems for Video Technology, v.17 n.3, p.313-323, March 2007 http://dx.doi.org/10.1109/TCSVT.2007.890634

[3] Ritendra Datta , Weina Ge , Jia Li , James Z. Wang, Toward Bridging the Annotation-Retrieval Gap in Image Search, IEEE MultiMedia, v.14 n.3, p.24-35, July 2007 http://dx.doi.org/10.1109/MMUL.2007.67

[4] Gustavo Carneiro , Antoni B. Chan , Pedro J. Moreno , Nuno Vasconcelos, Supervised Learning of Semantic Classes for Image Annotation and Retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.29 n.3, p.394-410, March 2007 http://dx.doi.org/10.1109/TPAMI.2007.61

[5] T. Athanasiadis , P. Mylonas , Y. Avrithis , S. Kollias, Semantic Image Segmentation and Object Labeling, IEEE Transactions on Circuits and Systems for Video Technology, v.17 n.3, p.298-312, March 2007 http://dx.doi.org/10.1109/TCSVT.2007.890636

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

[7] T. Sikora, The MPEG-7 visual standard for content description-an overview, IEEE Transactions on Circuits and Systems for Video Technology, v.11 n.6, p.696-702, June 2001 http://dx.doi.org/10.1109/76.927422

[8] Lei Wang , Latifur Khan, Automatic image annotation and retrieval using weighted feature selection, Multimedia Tools and Applications, v.29 n.1, p.55-71, April 2006 http://dx.doi.org/10.1007/s11042-006-7813-7

[9] Lokesh Setia , Hans Burkhardt, Feature selection for automatic image annotation, Proceedings of the 28th conference on Pattern Recognition, September 12-14, 2006, Berlin, Germany http://dx.doi.org/10.1007/11861898_30

[10] Xiaojun Qi , Yutao Han, Incorporating multiple SVMs for automatic image annotation, Pattern Recognition, v.40 n.2, p.728-741, February, 2007 http://dx.doi.org/10.1016/j.patcog.2006.04.042

[11] Lei Yu , Huan Liu, Efficient Feature Selection via Analysis of Relevance and Redundancy, The Journal of Machine Learning Research, 5, p.1205-1224, 12/1/2004 http://dl.acm.org/citation.cfm?id=1044700&CFID=558819604&CFTOKEN=68186175

[12] Jinjie Huang , Yunze Cai , Xiaoming Xu, A Wrapper for Feature Selection Based on Mutual Information, Proceedings of the 18th International Conference on Pattern Recognition, p.618-621, August 20-24, 2006 http://dx.doi.org/10.1109/ICPR.2006.198

[13] Manoranjan Dash , Huan Liu, Consistency-based search in feature selection, Artificial Intelligence, v.151 n.1-2, p.155-176, December 2003 http://dx.doi.org/10.1016/S0004-3702(03)00079-1

[14] Wei Jiang , Guihua Er , Qionghai Dai , Jinwei Gu, Similarity-based online feature selection in content-based image retrieval, IEEE Transactions on Image Processing, v.15 n.3, p.702-712, March 2006 http://dx.doi.org/10.1109/TIP.2005.863105

[15] Benjamín Hernández , Gustavo Olague , Riad Hammoud , Leonardo Trujillo , Eva Romero, Visual learning of texture descriptors for facial expression recognition in thermal imagery, Computer Vision and Image Understanding, v.106 n.2-3, p.258-269, May, 2007 http://dx.doi.org/10.1016/j.cviu.2006.08.012

[16] T.M. Hamdani, A.M. Alimi, F. Karray, Distributed genetic algorithm with bi-coded chromosomes and a new evaluation function for features selection, in: Proceeding of IEEE Congress on Evolutionary Computation, Canada, 2006, pp. 581-588.

[17] Huang, C.L. and Wang, C.J., A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications. v31 i2. 231-240.

[18] J. Amores , N. Sebe , P. Radeva, Boosting the distance estimation, Pattern Recognition Letters, v.27 n.3, p.201-209, February 2006 http://dx.doi.org/10.1016/j.patrec.2005.08.019http://dx.doi.org/10.1109/76.927424

[19] B. S. Manjunath , J. -R. Ohm , V. V. Vasudevan , A. Yamada, Color and texture descriptors, IEEE Transactions on Circuits and Systems for Video Technology, v.11 n.6, p.703-715, June 2001 [doi>10.1109/76.927424]

[20] M. Bober, MPEG-7 visual shape descriptors, IEEE Transactions on Circuits and Systems for Video Technology, v.11 n.6, p.716-719, June 2001 http://dx.doi.org/10.1109/76.927426

[21] ISO/IEC/JTC1/SC29/WG11/N4063, MPEG-7 visual experimentation model (XM), Version 10.0, ISO/IEC, 2001.

[22] Joni-Kristian Kamarainen , Ville Kyrki , Jarmo Ilonen , Heikki Kälviäinen, Improving similarity measures of histograms using smoothing projections, Pattern Recognition Letters, v.24 n.12, p.2009-2019, August 2003 http://dx.doi.org/10.1016/S0167-8655(03)00039-4

[23] Il-Seok Oh , Jin-Seon Lee , Byung-Ro Moon, Hybrid Genetic Algorithms for Feature Selection, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.26 n.11, p.1424-1437, November 2004 http://dx.doi.org/10.1109/TPAMI.2004.105

[24] Gao, Y.L., Fan, J.P., Luo, H.Z., Xue, X.Y. and Jain, R., Automatic image annotation by incorporating feature hierarchy and boosting to scale up SVM classifiers. ACM Multimedia. 901-910.


Full Text

internal file

Sonstige Links