Abstract
Automatic image annotation means employing learning models for describing visual contents of images by using text descriptors. With the fast growth of digital images in the web, large-scale automatic image annotation has started to deal with major challenges. The most important challenges are scalability and annotation performance. In this research, in order to solve scalability and the image annotation time challenge, the prototype selection approach is used. The assumption of the prototype selection is based on single-label instances while, in image annotation, an instance has more than one label. It means that instances are multi-label. Hence, to employ prototype selection algorithms in image annotation, focusing on the concept of multi-label is a critical task. Thus, taking an appropriate measure in these methods to compute the rate of dissimilarity between label vectors has a great importance. The proposed approach in this paper is based on multi-labeling of prototype selection methods by selecting a modifying appropriate binary dissimilarity measure, in comparison two label vectors. The effectiveness of the proposed approach in reducing the number of training instances and selecting effective ones has been shown by experiments on large-scale NUS-WIDE family image sets. The experimental results showed the effectiveness of the proposed approach in reducing the number of instances and improving annotation performance.
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Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6(1):37–66
Amiri SH, Jamzad M (2015) Efficient multi-modal fusion on supergraph for scalable image annotation. Pattern Recogn 48(7):2241–2253
Angiulli F (2007) Fast nearest neighbor condensation for large data sets classification. IEEE Trans Knowl Data Eng 19(11):1450–1464
Bandyopadhyay S (2005) An efficient technique for superfamily classification of amino acid sequences: feature extraction, fuzzy clustering and prototype selection. Fuzzy Sets Syst 152(1):5–16
Brighton H, Mellish C (2002) Advances in instance selection for instance-based learning algorithms. Data Min Knowl Discov 6(2):153–172
Cao J, Lin Z (2015) Extreme learning machines on high dimensional and large data applications: a survey. Math Probl Eng 501:103–796
Charte F, Rivera AJ, Del Jesus MJ, Herrera F (2014) Mlenn: a first approach to heuristic multilabel undersampling. In: Intelligent Data Engineering and Automated Learning–IDEAL 2014. Springer, pp 1–9
Chen X, Mu Y, Yan S, Chua TS (2010) Efficient large-scale image annotation by probabilistic collaborative multi-label propagation. In: Proceedings of the international conference on Multimedia. ACM, pp 35–44
Choi SS, Cha SH, Tappert CC (2010) A survey of binary similarity and distance measures. Journal of Systemics. Cybern Inf 8(1):43–48
Chua TS, Tang J, Hong R, Li H, Luo Z, Zheng Y (2009) Nus-wide: a real-world web image database from national university of singapore. In: Proceedings of the ACM international conference on image and video retrieval. ACM, p 48
Deng C, Liu X, Mu Y, Li J (2015) Large-scale multi-task image labeling with adaptive relevance discovery and feature hashing. Signal Process 112:137–145
Devi VS, Murty MN (2002) An incremental prototype set building technique. Pattern Recogn 35(2):505–513
Devijver PA (1986) On the editing rate of the multiedit algorithm. Pattern Recogn Lett 4(1):9–12
García S, Cano JR, Herrera F (2008) A memetic algorithm for evolutionary prototype selection: a scaling up approach. Pattern Recogn 41(8):2693–2709
Garcia S, Derrac J, Cano JR, Herrera F (2012) Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Trans Pattern Anal Mach Intell 34(3):417–435
García S, Luengo j, Herrera F (2015) Data preprocessing in data mining. Springer
García-Pedrajas N, Del Castillo JAR, Ortiz-Boyer D (2010) A cooperative coevolutionary algorithm for instance selection for instance-based learning. Mach Learn 78(3):381–420
Gates G (1972) The reduced nearest neighbor rule (Corresp.) IEEE Trans Inf Theory 18(3):431–433. doi:10.1109/TIT.1972.1054809
Hart P (1968) The condensed nearest neighbor rule (Corresp.) IEEE Trans Inf Theory 14(3):515–516. doi:10.1109/TIT.1968.1054155
Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529
Huang J, Liu H, Shen J, Yan S (2013) Towards efficient sparse coding for scalable image annotation. In: Proceedings of the 21st ACM international conference on Multimedia. ACM, pp 947–956
Kan M, Xu D, Shan S, Chen X (2014) Semisupervised hashing via kernel hyperplane learning for scalable image search. IEEE Trans Circ Syst Video Technol 24 (4):704–713
Ke X, Li S, Chen G (2013) Real web community based automatic image annotation. Comput Electr Eng 39(3):945–956
Kuncheva LI (1995) Editing for the k-nearest neighbors rule by a genetic algorithm. Pattern Recogn Lett 16(8):809–814
Li R, Lu J, Zhang Y, Lu Z, Xu W (2009) A framework of large-scale and real-time image annotation system. In: International Joint Conference on Artificial Intelligence, 2009. JCAI’09. IEEE, pp 576–579
Liu Y, Zhang D, Lu G, Ma WY (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recogn 40(1):262–282
Ma Z, Nie F, Yang Y, Uijlings JR, Sebe N (2012) Web image annotation via subspace-sparsity collaborated feature selection. IEEE Trans Multimed 14(4):1021–1030
Özgür A, Özgür L, Güngör T (2005) Text categorization with class-based and corpus-based keyword selection. In: Computer and Information Sciences-ISCIS 2005. Springer, pp 606–615
Riquelme JC, Aguilar-Ruiz JS, Toro M (2003) Finding representative patterns with ordered projections. Pattern Recogn 36(4):1009–1018
Sánchez JS, Pla F, Ferri FJ (1997) Prototype selection for the nearest neighbour rule through proximity graphs. Pattern Recogn Lett 18(6):507–513
Sierra B, Lazkano E, Inza I, Merino M, Larrañaga P, Quiroga J (2001) Prototype selection and feature subset selection by estimation of distribution algorithms. a case study in the survival of cirrhotic patients treated with tips. In: Artificial Intelligence in Medicine. Springer, pp 20–29
Skalak DB (1994) Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: Proceedings of the eleventh international conference on machine learning, pp 293– 301
Subramanya A, Bilmes JA (2009) Entropic graph regularization in non-parametric semi-supervised classification. In: Advances in Neural Information Processing Systems, pp 1803–1811
Tang J, Hong R, Yan S, Chua TS, Qi GJ, Jain R (2011) Image annotation by k nn-sparse graph-based label propagation over noisily tagged web images. ACM Trans Intell Syst Technol (TIST) 2(2):14
Tomek I (1976) An experiment with the edited nearest-neighbor rule. IEEETransactions on Systems, Man, and Cybernetics 6:448–452
Vijaya P, Murty MN, Subramanian D (2003) Supervised k-medians algorithm for prototype selection for protein sequence classification. In: Proceedings of the International Conference on Advances in Pattern Recognition. Allied Press, India, pp 129–132
Wang F (2011) A survey on automatic image annotation and trends of the new age. Proced Eng 23:434–438
Wang F, Zhang C (2008) Label propagation through linear neighborhoods. IEEE Trans Knowl Data Eng 20(1):55–67
Wang S, Huang Q, Jiang S, Tian Q (2012) Scalable semi-supervised multiple kernel learning for real-world image applications, vol 14, pp 1259–1274
Wilson DL (1972) Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans Syst Man Cybern 3:408–421
Wilson DR, Martinez TR (2000) Reduction techniques for instance-based learning algorithms. Mach Learn 38(3):257–286
Yang Y, Wu F, Nie F, Shen HT, Zhuang Y, Hauptmann AG (2012) Web and personal image annotation by mining label correlation with relaxed visual graph embedding. IEEE Trans Image Process 21(3):1339–1351
Yuan Y, Wu F, Shao J, Zhuang Y (2013) Image annotation by semi-supervised cross-domain learning with group sparsity. J Vis Commun Image Represent 24(2):95–102
Zare Chahooki MA, Hamid KS (2015) Using manifold structure for automatic image annotation by fusion of multiple feature spaces. J Commun Technol Electron Comput Sci 2:1–5
Zhang D, Li WJ (2014) Large-scale supervised multimodal hashing with semantic correlation maximization. In: Proceedings of the 28th AAAI conference on artificial intelligence. AAAI, Quebec, pp 2177–2183
Zhang H, Berg AC, Maire M, Malik J (2006) Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In: 2006 IEEE computer society conference on Computer vision and pattern recognition, vol 2. IEEE, pp 2126–2136
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Shooroki, H.K., Zare Chahooki, M.A. Selection of effective training instances for scalable automatic image annotation. Multimed Tools Appl 76, 9643–9666 (2017). https://doi.org/10.1007/s11042-016-3572-2
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DOI: https://doi.org/10.1007/s11042-016-3572-2