Abstract
In order to integrate talking and reading with an NAO robot to excite the communication willingness of autistic children, the robot should make appropriate book choice. In this paper, a picture book was divided into the textual information and the image information, we proposed a new approach for picture books training set selection, which combined the multi-modal information of each picture book to select representative and integral training samples for capturing sufficient picture books by correspondence analysis. In our provided method, an improved chi-square statistic to get relative terms and an near-duplicated keyframe mining method to get image information are proposed. Finally, the experimental results demonstrated the feasibility. Moreover, our approach can cut down the computational costs and perform well compared with the manual selection method and the method based on the Q-factor analysis.


Similar content being viewed by others
References
Feng, H., Gutierrez, A., Zhang, J., Mahoor, M.H.: Can NAO robot improve eye-gaze attention of children with high functioning autism?,’ in Healthcare Informatics (ICHI). IEEE Int. Conf. IEEE 2013, 484 (2013)
Human–robot interaction, https://en.wikipedia.org/wiki/Human–robot interaction
Tapus, A., Peca, A., Aly, A., Pop, C., Jisa, L., Pintea, S., et al.: Children with autism social engagement in interaction with Nao, an imitative robot a series of single case experiments. Int. Stud. 13, 315–347 (2012)
Makhtar, A.K., Yussof, H., Al-Assadi, H., Yee, L.C., Shamsuddin, S., Yussof, H.: Humanoid robot NAO interacting with autistic children of moderately impaired intelligence to augment communication skills. Proc. Eng. 41, 1533–1538 (2012)
Bekele, E.T., Lahiri, U., Swanson, A.R., Crittendon, J.A., Warren, Z.E., Sarkar, N.: A step towards developing adaptiverobot-mediatedintervention architecture (ARIA) for children with autism. IEEE Transact. Neural Systems Rehabilit. Eng. 21, 289–299 (2013)
Shamsuddin, S., Yussof, H., Ismail, L., Hanapiah, F.A., Mohamed, S., Piah, H.A., et al., Initial response of autistic children in human-robot interaction therapy with humanoid robot NAO. In Signal Processing and its Applications (CSPA), 2012 IEEE 8th International Colloquium on, 2012, pp. 188–193
http://news.psu.edu/story/141114/2007/04/09/research/story-power-impact-childrens-literature
Núñez-Valdez, E.R., Lovelle, J.M.C., Hernández, G.I., Fuente, A.J.: Comput. Human Beh. 51, 1320–1330 (2015)
Liu, J., Tian, Y., Yu, X., Yang, Z., Jia, X., Ma, C., Xu, Z.: A multi-source approach for bug triage. Int. J. Software Eng. Knowl. Eng. 26(9–10), 1593–1604 (2016)
Xu, Z., Liu, Y., Zhang, H., Luo, X., Mei, L., Hu, C.: Building the multi-modalstorytelling of urban emergency events based on crowdsensing of social media analytics. MONET 22(2), 218–227 (2017)
Xu, Z., Mei, L., Lu, Z., Hu, C., Luo, X., Zhang, H., Liu, Y.: Multi-modal description of public security events using surveillance and social data. IEEE Transact. Big Data (2017). doi:10.1109/TBDATA.2017.2656918
Merz, C.J.: Using correspondence analysis to combine classifiers. Mac. Learning 36(1), 33–58 (1999)
Hosoi, H., Yamagata, T., Ikarashi, Y., Fujisawa, N.: Visualization of Special Features in “The Tale of Genji” by Text Mining and Correspondence Analysis with Clustering. J. Flow Control Meas. Visual. 2(1), 1–6 (2014)
Liu, C., Wang, W., Wang, M., Lv, F., Konan, M.: An efficient instance selection algorithm to reconstruct training set for support vector machine. Knowledge-Based Systems 116, 58–73 (2017)
Stojanović, Miloš B., Božić, Miloš M., Stanković, Milena M., Stajić, Zoran P.: A methodology for training set instance selection using mutual information in time series prediction. Neurocomputing 141, 236–245 (2014)
Gura, TC., Software development project rejection: A quantitative Q-factor analysis to measure cognitive learning styles of medical doctors and programmers, Dissertations & Theses - Gradworks, 2014
Gheyas, I.A.: Feature subset selection in large dimensionality domains. Pattern Recog. 43, 5–13 (2010)
Shyu, M.L., Xie, Z., Chen, M., Chen, S.C.: Video semantic event/concept detection using a subspace-based multimedia data mining framework. IEEE Transact. Multimedia 10, 252–259 (2008)
Yang, Y., Dai, B.: Chinese keyword extraction method based on multi-features. Comput. Appl. Software 31, 109–112 (2014)
Forman, G.: An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289–1305 (2003)
Mohanaiah, P., Sathyanarayana, P., GuruKumar, L.: Image texture feature extraction using GLCM approach. Int. J. Sci. Res. Publ. 3, 1–5 (2013)
Devi, B.S., Shanmugavadivu, R.: Picture component technique based SVM classification and GLCM for diagnosis and detection of dermoscopic images. Int. J. Adv. Res. Comp. Comm. Eng. 5, 401 (2016)
C.-W. Ngo, W.-L. Zhao, and Y.-G. Jiang, “Fast tracking of near-duplicate keyframes in broadcast domain with transitivity propagation,” presented at the Proceedings of the 14th ACM international conference on Multimedia, Santa Barbara, CA, USA, 2006
Y. Ke, R. Sukthankar, and L. Huston, “Efficient Near-duplicate Detection and Sub-image Retrieval,” in ACM International Conference on Multimedia (MM), New York, 2004
Zhao, W.-L., Ngo, C.W., Tan, H.K., Wu, X.: Near-duplicate klyframe identification with interest point matching and pattern learning. IEEE Trans. Multimedia 9, 1037–1048 (2007)
Zhang, D.-Q., Lin, C.-Y., Chang, S.-F., Smith, J.R.: Semantic video clustering across sources using bipartite spectral clustering. IEEE Int. Conf. Multimedia 1, 117–120 (2004)
Li, X., Snoek, C.G.M., Worring, M.: Learning social tag relevance by neighbor voting. IEEE Transact. Multimedia 11, 1310–1322 (2009)
Varimax rotation. https://en.wikipedia.org/wiki/Varimax_rotation
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, X., Yu, HQ., Sun, SM. et al. Using correspondence analysis to select training set for multi-modal information data. Cluster Comput 21, 893–905 (2018). https://doi.org/10.1007/s10586-017-0945-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-017-0945-x