Abstract
This paper introduces an unsupervised graph-based rank aggregation approach for event prediction. The solution is based on the encoding of multiple ranks of a query, defined according to different criteria, into a graph. Later, we embed the generated graph into a feature space, creating fusion vectors. These vectors are then used to train a predictor to determine if an input (even multimodal) object refers to an event or not. Performed experiments in the context of the flooding detection task of the MediaEval 2017 shows that the proposed solution is highly effective for different detection scenarios involving textual, visual, and multimodal features, yielding better detection results than several state-of-the-art methods.
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References
Ahmad, K., Pogorelov, K., Riegler, M., Conci, N., Pal, H.: CNN and GAN based satellite and social media data fusion for disaster detection. In: MediaEval Workshop, Dublin (2017)
Ahmad, S., Ahmad, K., Ahmad, N., Conci, N.: Convolutional neural networks for disaster images retrieval. In: MediaEval Workshop, Dublin (2017)
Avgerinakis, K., et al.: Visual and textual analysis of social media and satellite images for flood detection@ multimedia satellite task MediaEval 2017. In: MediaEval Workshop, Dublin (2017)
Bischke, B., Bhardwaj, P., Gautam, A., Helber, P., Borth, D., Dengel, A.: Detection of flooding events in social multimedia and satellite imagery using deep neural networks. In: MediaEval Workshop, Dublin (2017)
Bischke, B., Helber, P., Schulze, C., Venkat, S., Dengel, A., Borth, D.: The multimedia satellite task at MediaEval 2017: emergence response for flooding events. In: MediaEval Workshop, Dublin (2017)
Chatzichristofis, S.A., Boutalis, Y.S.: CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 312–322. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79547-6_30
Dao, I.S., Minh, P.Q.N., Kasem, A.: A context-aware late-fusion approach for disaster image retrieval from social media. In: ICMR 2018. ACM (2018)
Dao, M.S., Pham, Q.N.M., Nguyen, D., Tien, D.: A domain-based late-fusion for disaster image retrieval from social media. In: MediaEval Workshop, Dublin (2017)
Dourado, I.C., Pedronette, D.C.G., da Silva Torres, R.: Unsupervised graph-based rank aggregation for improved retrieval. Inf. Process. Manage. 56(4), 1260–1279 (2019). https://doi.org/10.1016/j.ipm.2019.03.008. ISSN 0306-4573
Fu, X., Bin, Y., Peng, L., Zhou, J., Yang, Y., Shen, H.T.: BMC@MediaEval 2017 multimedia satellite task via regression random forest. In: MediaEval Workshop, Dublin (2017)
Hanif, M., Tahir, M.A., Khan, M., Rafi, M.: Flood detection using social media data and spectral regression based kernel discriminant analysis. In: MediaEval Workshop, Dublin (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: Proceedings of IEEE CVPR 1997, pp. 762–768. IEEE (1997)
Kornblith, S., Shlens, J., Le, Q.V.: Do better imagenet models transfer better? arXiv preprint arXiv:1805.08974 (2018)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)
Lopez-Fuentes, L., van de Weijer, J., Bolanos, M., Skinnemoen, H.: Multi-modal deep learning approach for flood detection. In: MediaEval Workshop, Dublin (2017)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Nogueira, K., et al.: Data-driven flood detection using neural networks. In: MediaEval Workshop, Dublin (2017)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Tkachenko, N., Zubiaga, A., Procter, R.: WISC at MediaEval 2017: multimedia satellite task. In: MediaEval Workshop, Dublin (2017)
Werneck, R.O., Dourado, I.C., Fadel, S.G., Tabbone, S., Torres, R.S.: Graph-based early-fusion for flood detection. In: 25th IEEE ICIP, pp. 1048–1052. IEEE (2018)
Zhang, S., Yang, M., Cour, T., Yu, K., Metaxas, D.N.: Query specific rank fusion for image retrieval. IEEE PAMI 37(4), 803–815 (2015)
Zhao, Z., Larson, M.: Retrieving social flooding images based on multimodal information. In: MediaEval Workshop, Dublin (2017)
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1452–1464 (2018). https://doi.org/10.1109/TPAMI.2017.2723009. ISSN 0162-8828
Zhou, W., Li, H., Tian, Q.: Recent advance in content-based image retrieval: a literature survey. CoRR abs/1706.06064 (2017)
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. arXiv preprint arXiv:1707.07012 (2017)
Acknowledgments
Authors are grateful to CNPq (grant #307560/2016-3), São Paulo Research Foundation – FAPESP (grants #2014/12236-1, #2015/24494-8, #2016/50250-1, and #2017/20945-0) and the FAPESP-Microsoft Virtual Institute (grants #2013/50155-0, and #2014/50715-9). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
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Dourado, I.C., Tabbone, S., Torres, R.d.S. (2019). Event Prediction Based on Unsupervised Graph-Based Rank-Fusion Models. In: Conte, D., Ramel, JY., Foggia, P. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2019. Lecture Notes in Computer Science(), vol 11510. Springer, Cham. https://doi.org/10.1007/978-3-030-20081-7_9
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