Abstract
With the development of event-based social network, the ways and tools of people’s online and offline social activities have changed dramatically. IEBSN emphasizes the impromptu of social events, which will lead to inaccurate and inefficient event recommendation. To address this problem, we propose a novel dynamic scene recognition method for IEBSN, which combines supervised learning and unsupervised learning. Aiming at the similarity and inconsistency between classes in scene recognition, we propose a convolution feature encoder, which can extract more scene visual information. In order to meet the unexpected requirement of application level, the images whose convolution module is lower than a certain threshold are clustered with k-means. Experiments show that this method can automatically identify the scene in IEBSN application efficiently, and alleviate the problems in scene recognition.
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Chattopadhyay, B., Bhattacharya, S., Forsgren, B.K., Kanumuri, S., Teplitsky, S.: Dynamic group and event update method in phone based impromptu meet-up app. US Patent 9,692,795, 27 June 2017
Guo, N., Lu S.: Event recommendation in impromptu event-based social networks, pp. 1–5 (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep conventional networks. In: NIPS, pp. 1106–1114 (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Li, F.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 252 (2015)
Simonyan, K., Zisserman, A.: Very deep conventional networks for large-scale image recognition. In: ICLR, pp. 1–14 (2015)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with conventions. In: CVPR, pp. 1–9 (2015)
Li F., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: CVPR, pp. 524–531 (2005)
Zhang, L., Zhen, X., Shao, L.: Learning object-to-class kernels for scene classification. IEEE Trans. Image Process. 23(8), 3241–3253 (2014)
Wu, J., Rehg, J.M.: CENTRIST: a visual descriptor for scene classification. IEEE Trans. Pattern Anal. Mach. Intel. 33(8), 1489–1501 (2011)
Gong, Y., Wang, L., Guo, R., Lazebnik, S.: Multi-scale ordered pooling of deep convective activation features. In: ECCV, pp. 392–407 (2014)
Xie, L., Wang, J., Guo, B., Zhang, B., Tian, Q.: Oriental pyramid matching for recognizing index scenes. In: CVPR, pp. 3734–3741 (2014)
Zuo, Z., Wang, G., Shuai, B., Zhao, L., Yang, Q., Jiang, X.: Learning discriminatory and shareable features for scene classification. In: ECCV, pp. 552–568 (2014)
Yang, S., Ramanan, D.: Multi-scale recognition with DAG-CNNs. In: ICCV, pp. 1215–1223 (2015)
Shen, L., Lin, Z., Huang, Q.: Relay backpropagation for effective learning of deep conventional neural networks. In: ECCV, pp. 467–482 (2016)
Zuo, Z., Shuai, B., Wang, G., Liu, X., Wang, X., Wang, B., Chen, Y.: Learning contextual dependence with conventional hierarchical recurrent neural networks. IEEE Trans. Image Process. 25(7), 2983–2996 (2016)
Guo, S., Huang, W., Wang, L., Qiao, Y.: Locally supervised deep model for scene recognition. IEEE Trans. Image Process. 26(2), 808–820 (2017)
Wang, Z., Wang, L., Wang, Y., Zhang, B., Qiao, Y.: Weakly supervised patchnets: describing and aggregating local patches for scene recognition. In: CoRR, vol. abs/1609.00153 (2016)
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: CVPR, vol. 2, pp. 2169–2178 (2006)
Xiong, Y., Zhu, K., Lin, D., Tang, X.: Recognize complex events from static images by fusing deep channels. In: CVPR, pp. 1600–1609 (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding conventional networks. In: ECCV, pp. 818–833 (2014)
Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: CVPR, pp. 5188–5196 (2015)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2014)
Herranz, L., Jiang, S., Li, X.: Scene recognition with CNNs: objects, scales and dataset bias (2018)
Zhou, B., Khosla, A., Lapedriza, A., Torralba, A., Oliva, A.: Places: An image database for deep scene understanding. In: CoRR, vol. Abs/1610.02055 (2016)
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Kang, H., Gao, T., Guo, N. (2020). A Dynamic Scene Recognition Method for Event-Based Social Network. In: Barolli, L., Xhafa, F., Hussain, O. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2019. Advances in Intelligent Systems and Computing, vol 994. Springer, Cham. https://doi.org/10.1007/978-3-030-22263-5_37
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DOI: https://doi.org/10.1007/978-3-030-22263-5_37
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