Abstract
Image annotation technique can be formulated as a multi-class classification problem, which can be solved by the ensemble of multiple class-pair classifiers. Support vector machine (SVM) classifiers based on optimal class-pair feature subsets from the multimedia content description interface (MPEG-7) standard are used as the class-pair classifiers. We use a binary-coded chromosome genetic algorithm (GA) to select optimal class-pair feature subsets, and a bi-coded chromosome GA to simultaneously select optimal class-pair feature subsets and corresponding optimal weight subsets, i.e. optimal class-pair weighted feature subsets. We consider two kinds of methods for class-pair feature selection: a common optimal (or weighted) feature subset is selected for all the class-pairs, and an individual optimal (or weighted) feature subset is selected for each class-pair respectively. Majority voting scheme is used to combine the class-pair SVM classifiers. The experiments are performed on two different image sets to validate the performance of our image annotation techniques.
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References
Anagnostopoulos A, Broder A, Punera K (2008) Effective and efficient classification on a search-engine model. Knowl Inform Syst 16(2): 129–154
Bober M (2001) MPEG-7 visual shape descriptor. IEEE Trans Circuits Syst Video Technol 11(6): 716–719
Carneiro G, Chan AB, Moreno PJ, Vasconcelos N (2007) Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans Pattern Anal Mach Intell 29(3): 394–410
Chang E, Goh KS, Sychay G, Wu G (2003) CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines. IEEE Trans Circuits Syst Video Technol 13(1): 26–38
Chang SF, Chen W, Sundaram H (1998) Semantic visual templates: linking visual features to semantics. In: Proceedings of the IEEE international conference of image processing, pp 531–535
Chi MM, Bruzzone L (2006) An ensemble-driven k-NN approach to ill-posed classification problems. Pattern Recognit Lett 27(4): 301–307
Chiang TW, Tsai T (2008) Query color images using user specified wavelet features. Knowl Inform Syst 15(1): 109–129
Datta R, Joshi D, Wang J (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2): 1–60
Djordjevic D, Izquierdo E (2007) An object-and user-driven system for semantic-based image annotation and retrieval. IEEE Trans Circuits Syst Video Technol 17(3): 313–323
Duygulu P, Banard K, de Freitas JFG, Forsyth DA (2002) Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. In: Proceedings of the 7th European conference on computer vision, pp 97–112
Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1): 119–139
Gao YL, Fan JP, Luo HZ, Xue XY, Jain R (2006) Automatic image annotation by incorporating feature hierarchy and boosting to scale up SVM classifiers. In: Proceedings of the 14th international conference of multimedia, pp 901–910
Garcia-Pedrajas N, Ortiz-Boyer D (2006) Improving multiclass pattern recognition by the combination of two strategies. IEEE Trans Pattern Anal Mach Intell 28(6): 1001–1006
Goh KS, Chang EY, Li B (2005) Using one-class and two-class SVMs for multiclass image annotation. IEEE Trans Knowl Data Eng 17(10): 1333–1346
Hamdani TM, Alimi AM, Karray F (2006) Distributed genetic algorithm with bi-coded chromosomes and a new evaluation function for features selection. In: Proceedings of the IEEE congress on evolutionary computation, pp 581–588
Huang SH, Wu QJ, Lai SH (2006) Improved AdaBoost-based image retrieval with relevance feedback via paired feature learning. Multimedia Syst 12(1): 14–26
Jeon J, Lavrenko V, Manmatha R (2003) Automatic image annotation and retrieval using cross-media relevance models. In: Proceedings of SIGIR conference on research and development in information retrieval, pp 119–126
Katakis I, Tsoumakas G, Vlahavas I (2009) Tracking recurring contexts using ensemble classifiers: an application to email filtering. Knowl Inform Syst (in press)
Li J, Wang JZ (2003) Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans Pattern Anal Mach Intell 25(19): 1075–1088
Li W, Sun MS (2006) Incorporating prior knowledge into multi-label boosting for cross-modal image annotation and retrieval. Lect Notes Comput Sci 4182: 404–415
Liu XZ, Zhang L, Li MJ, Zhang HJ, Wang DX (2005) Boosting image classification with LDA-based feature combination for digital photograph management. Pattern Recognit 38(6): 887–901
Liu Y, Zhang D, Lu GJ, Ma WY (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recognit 40(4): 262–282
Manjunath BS, Ohm JR, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11(6): 703–715
Oh IS, Lee JS, Moon BR (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Pattern Anal Mach Intell 26(11): 1424–1437
Ou GB, Murphey YL (2007) Multi-class pattern classification using neural networks. Pattern Recognit 40(1): 4–18
Peng T, Zuo WL, He FL (2008) SVM based adaptive learning method for text classification from positive and unlabeled documents. Knowl Inform Syst 16(3): 281–301
Qi XJ, Han YT (2007) Incorporating multiple SVMs for automatic image annotation. Pattern Recognit 40(4): 728–741
Shi R, Chua TS, Lee CH, Gao S (2006) Bayesian learning of hierarchical multinomial mixture models of concepts for automatic image annotation. Lect Notes Comput Sci 4071: 102–112
Tieu K, Viola P (2000) Boosting image retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 228–235
Wang BX, Japlowicz N (2009) Boosting support vectors for imbalanced data sets. Knowl Inform Syst (in press)
Zhuang Y, Liu X, Pan Y (1999) Apply semantic template to support content-based image retrieval. Lect Notes Comput Sci 3972: 442–449
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Lu, J., Li, R., Zhang, Y. et al. Image annotation techniques based on feature selection for class-pairs. Knowl Inf Syst 24, 325–337 (2010). https://doi.org/10.1007/s10115-009-0240-0
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DOI: https://doi.org/10.1007/s10115-009-0240-0