Dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation

Knowledge-Based Systems

Volumn 23, Issue 3, 2010, Pages 195-201

  Li, Ran ^a   Lu, Jianjiang ^a   Zhang, Yafei ^a   Zhao, Tianzhong ^a  

^a PLA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Adaboost learning; Feature selection; Image annotation; Parallel genetic algorithm

Indexed keywords

ADABOOST LEARNING; BASE CLASSIFIERS; ENSEMBLE CLASSIFIERS; ENSEMBLE MEMBERS; FEATURE SELECTION; FEATURE SELECTION METHODS; FEATURE SUBSET; FEATURE SUBSET SELECTION; IMAGE ANNOTATION; K-NEAREST NEIGHBOR CLASSIFIER; MASTER-SLAVE; OPTIMAL WEIGHT; PARALLEL GENETIC ALGORITHMS; PARALLEL PROGRAM; SUBSET SELECTION; WEAK CLASSIFIERS; WEIGHTED FEATURES;

CHROMOSOMES; CLASSIFIERS; COMPUTATIONAL EFFICIENCY; FEATURE EXTRACTION; GENETIC ALGORITHMS; IMAGE ANALYSIS; MOTION PICTURE EXPERTS GROUP STANDARDS; OPTIMIZATION; SET THEORY;

ADAPTIVE BOOSTING;

EID: 77549085570     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2009.11.020     Document Type: Article

References (54)

1. Liu Y., Zhang D., Lu G.J., and Ma W.Y. A survey of content-based image retrieval with high-level semantics. Pattern Recognition 40 4 (2007) 262-282
2. Datta R., Joshi D., and Wang J. Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys 40 2 (2008) 1-60
3. Tsai C.F., and Hung C. Automatically annotating Images with keywords: a review of image annotation systems. Recent Patents on Computer Science 1 1 (2008) 55-68
4. Djordjevic D., and Izquierdo E. An object-and user-driven system for semantic-based image annotation and retrieval. IEEE Transactions on Circuits and Systems for Video Technology 17 3 (2007) 313-323
5. Carneiro G., Chan A.B., Moreno P.J., and Vasconcelos N. Supervised learning of semantic classes for image annotation and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 29 3 (2007) 394-410
6. Barnard K., Duygulu P., Forsyth D., de Freitas N., Blei D., and Jordan M. Matching words and pictures. Journal of Machine Learning Research 3 (2003) 1107-1135
7. Chang E., Goh K.S., Sychay G., and Wu G. CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines. IEEE Transactions on Circuits and Systems for Video Technology 13 1 (2003) 26-38
8. Li J., and Wang J.Z. Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transactions Pattern Analysis and Machine Intelligence 25 19 (2003) 1075-1088
9. Yang S.J., Kim S.K., Seo K.S., Ro Y.M., Kim J.Y., and Seo Y.S. Semantic categorization of digital home photo using photographic region templates. Information Processing and Management 43 2 (2007) 503-514
10. Lu J.J., Zhao T.Z., and Zhang Y.F. Feature selection based-on genetic algorithm for image annotation. Knowledge-Based Systems 21 (2008) 887-891
11. Y.L. Gao, J.P. Fan, H.Z. Luo, X.Y. Xue, R. Jain, Automatic image annotation by incorporating feature hierarchy and boosting to scale up SVM classifiers, in: Proceeding of Multimedia, 2006, pp. 901-910.
12. Hernández B., Olague G., Hammoud R., Trujillo L., and Romero E. Visual learning of texture descriptors for facial expression recognition in thermal imagery. Computer Vision and Image Understanding 106 2-3 (2007) 258-269
13. Setia L., and Burkhardt H. Feature selection for automatic image annotation. Lecture Notes in Computer Science 4174 (2006) 294-303
14. Tsymbal A., Puuronen S., and Patterson D.W. Ensemble feature selection with the simple Bayesian classification. Information Fusion 4 2 (2003) 87-100
15. Redpath D.B., and Lebart K. Boosting feature selection. Lecture Notes in Computer Science 3686 (2005) 305-314
16. Yin X.C., Liu C.P., and Han Z. Feature combination using boosting. Pattern Recognition Letters 26 14 (2005) 2195-2205
17. Oliveira L.S., Morita M., and Sabourin R. Feature selection for ensembles applied to handwriting recognition. International Journal of Document Analysis 8 4 (2006) 262-279
18. Y. Freund, R. Schapire, Experiments with a new boosting algorithm, in: Proceedings of 13th International Conference on Machine Learning, 1996, pp. 148-156.
19. J.L. Tu, Z.Q. Zhang, Z.H. Zeng, T. Huang, Face localization via hierarchical CONDENSATION with fisher Boosting feature selection, in: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04), 2004, pp. 719-724.
20. R. Xiao, W.J. Li, Y.D. Tian, X.O. Tang, Joint Boosting feature selection for robust face recognition, in: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, pp. 1415-1422.
21. J.R. Quinlan, Bagging, boosting and c4.5, in: Proceedings of the Thirteenth National Conference on Artificial Intelligence, 1996, pp. 725-730.
22. Bauer E., and Kohavi R. An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning 36 1-2 (1999) 105-139
23. C. Guerra-Salcedo, D. Whitley, Feature selection mechanisms for ensemble creation: a genetic search perspective, AAAI-99 1999, pp.13-17.
24. Ruta D., and Gabrys B. An overview of classifier fusion methods. Computing and Information Systems 7 1 (2000) 1-10
25. Qi X.J., and Han Y.T. Incorporating multiple SVMs for automatic image annotation. Pattern Recognition 40 4 (2007) 728-741
26. Chi M.M., and Bruzzone L. An ensemble-driven k-NN approach to ill-posed classification problems. Pattern Recognition Letters 27 4 (2006) 301-307
27. Ou G.B., and Murphey Y.L. Multi-class pattern classification using neural networks. Pattern Recognition 40 1 (2007) 4-18
28. Chen Y., and Wang J.Z. Image categorization by learning and reasoning with regions. The Journal of Machine Learning Research 5 8 (2004) 913-939
29. Ho T. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 8 (1998) 832-844
30. Gunter S., and Bunke H. Feature selection algorithms for the generation of multiple classifier systems and their application to handwritten word recognition. Pattern Recognition 25 11 (2004) 1323-1336
31. Tao D.C., Tang X.O., Li X.L., and Wu X.D. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 7 (2006) 1088-1099
32. W. Jiang, G.H. Er, Q.H. Dai, Boost SVM active learning for content-based image retrieval, in: Proceedings of the 37th IEEE International Conference on Signals, Systems and Computers, 2003, pp. 1585-1589.
33. N.R. Howe, Boosted image classification: an empirical study, in: Proceedings of the ICML Workshop on Machine Learning in Computer Vision, 2002.
34. B. Li, K. Goh, E.Y. Chang, Confidence-based dynamic ensemble for image annotation and semantics discovery, in: Proceedings of the ACM International Conference on Multimedia, 2003, pp. 528-538.
35. G.Z. Li, T.Y. Liu, Feature selection for bagging of Support Vector Machines, Lecture Notes in Computer Science, vol. 4099, 2006, pp. 404-415.
36. H. Feng, T.S. Chua, A bootstrapping approach to annotating large image collection, in: Proceedings of the ACM Multimedia Workshop on Multimedia Information Retrieval, 2003, pp. 55-62.
37. Li W., and Sun M.S. Incorporating prior knowledge into multi-label boosting for cross-modal image annotation and retrieval. Lecture Notes in Computer Science 4182 (2006) 404-415
38. Liu X.Z., Zhang L., Li M.J., Zhang H.J., and Wang D.X. Boosting image classification with LDA-based feature combination for digital photograph management. Pattern Recognition 38 6 (2005) 887-901
39. K. Tieu, P. Viola, Boosting image retrieval, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2000, pp. 228-235.
40. Huang S.H., Wu Q.J., and Lai S.H. Improved AdaBoost-based image retrieval with relevance feedback via paired feature learning. Multimedia Systems 12 1 (2006) 14-26
41. Schapire R., and Singer Y. Improved boosting algorithms using confidence-rated predictions. Machine Learning 37 3 (1999) 297-336
42. Manjunath B.S., Ohm J.R., Vasudevan V.V., and Yamada A. Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology 11 6 (2001) 703-715
43. Bober M. MPEG-7 visual shape descriptor. IEEE Transactions on Circuits and Systems for Video Technology 11 6 (2001) 716-719
44. ISO/IEC/JTC1/SC29/WG11/N4063, MPEG-7 visual experimentation model (XM), Version 10.0, ISO/IEC, 2001.
45. Cheng Y.C., and Chen S.Y. Image classification using, color, texture and regions. Image and Vision Computing 21 9 (2003) 759-776
46. Kamarainen J.K., Kyrki V., Ilonen J., and Kalviainen H. Improving similarity measures of histograms using smoothing projections. Pattern Recognition Letters 24 12 (2003) 2009-2019
47. Cantú-Paz E. A survey of parallel genetic algorithms. Calculateurs Paralleles 10 2 (1998) 141-171
48. Liu J., Iba H., and Ishizuka M. Selecting informative genes with parallel genetic algorithms in tissue classification. Genome Informatics 12 (2001) 14-23
49. Lim D.D., Ong Y.S., Jin Y.C., Sendho B., and Lee B.S. Efficient hierarchical parallel genetic algorithms using grid computing. Future Generation Computer Systems 23 4 (2007) 658-670
50. Oh I.S., Lee J.S., and Moon B.R. Hybrid genetic algorithms for feature selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 11 (2004) 1424-1437
51. T.M. Hamdani, A.M. Alimi, F. Karray, Distributed genetic algorithm with bi-coded chromosomes and a new evaluation function for features selection, in: Proceedings of the IEEE Congress on Evolutionary Computation, Canada, 2006, pp. 581-588.
52. Freund Y., and Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and Systems Science 55 1 (1997) 119-139
53. Tianzhong Zhao, Jianjiang Lu, Yafei Zhang, Qi Xiao, Weiguang Xu, Classifier ensemble based-on adaBoost and genetic algorithm for automatic image annotation, in: Proceedings of the IEEE International Conference on Information and Automation, 2008, pp. 1469-1473.
54. Ran Li, Tianzhong Zhao, Jianjiang Lu, Yafei Zhang, Weiguang Xu, Adaboost learning based-on sharing features and genetic algorithm for image annotation, in: Proceedings of Fourth International Conference on Natural Computation, 2008, pp. 402-406.