Conceptual semantic enhanced representation learning for event recognition in still images

References

• Adhikari, A., Dutta, B., Dutta, A., Mondal, D., & Singh, S. (2018). An intrinsic information content-based semantic similarity measure considering the disjoint common subsumers of concepts of an ontology. Journal of the Association for Information Science and Technology, 69(8), 1023–1034. https://doi.org/10.1002/asi.24021
   Web of Science ®Google Scholar
• Ahmad, K., Conci, N., Boato, G., & Francesco, G. D. N. (2016a). USED: A large-scale social event detection dataset. Proceedings of the International Conference on Multimedia Systems(MMSys), 50, 1–6. https://doi.org/10.1145/2910017.2910624
   Google Scholar
• Ahmad, K., Conci, N., & FGB, D. N. (2018). A saliency-based approach to event recognition. Signal Processing: Image Communication, 60, 42–51. https://doi.org/10.1016/j.image.2017.09.009
   Web of Science ®Google Scholar
• Ahmad, K., Mekhalfi, M., Conci, N., Boato, G., Melgani, F., & FGB, D. N. (2017). A pool of deep models for event recognition. Proceedings of the IEEE International Conference on Image Processing(ICIP), 2886–2890. https://doi.org/10.1109/ICIP.2017.8296810
   Google Scholar
• Ahmad, K., Natale, F. D., Boato, G., & Rosani, A. (2016b). A hierarchical approach to event discovery from single images using MIL framework. Proceedings of the IEEE Global Conference on Signal and Information Processing (Global SIP) (pp. 1223–1227). https://doi.org/10.1109/GlobalSIP.2016.7906036
   Google Scholar
• Akilandeswari, J., Jothi, G., Naveenkumar, A., Sabeenian, R. S., Iyyanar, P., & Paramasivam, M. E. (2021). Detecting pulmonary embolism using deep neural networks. International Journal of Performability Engineering, 17(3), 322–332. https://doi.org/10.23940/ijpe.21.03.p8.322332
   Google Scholar
• Ballester, P., & Matsumura, R. (2016). On the performance of GoogLeNet and AlexNetApplied to sketches. Proceedings of the Association for the Advance of Artificial Intelligence (AAAI) (pp. 1124–1128). http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12278
   Google Scholar
• Bhavana, D., Kishore, K. K., Chandra, M. B., Joy, S. D., & Mohan, G. G. (2021). Hand sign recognition using CNN. International Journal of Performability Engineering, 17(3), 314–321. https://doi.org/10.23940/ijpe.21.03.p7.314321
   Google Scholar
• Bossard, L., & Guillaumin, M. (2013). Event recognition in photo collections with a stopwatch HMM. Proceedings of the IEEE International Conference on Computer Vision(ICCV), 1193–1200. https://doi.org/10.1109/ICCV.2013.151
   Google Scholar
• Chang, C., & Lin, C. (2011). LIBSVM: A library for support vector machines. ACM TIST, 2(3), 27. https://doi.org/10.1145/1961189.1961199
   Google Scholar
• Dao, M., Tien, D., Nguyen, D., & Francesco, G. D. N. (2014). Robust event discovery from photo collections using signature image bases(SIBs). Multimedia Tools and Applications, 70(1), 25–53. https://doi.org/10.1007/s11042-012-1153-6
   Web of Science ®Google Scholar
• Deng, J., Dong, W., Socher, R., Li, L., Li, K., & Li, F. (2009). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 248–255). https://doi.org/10.1109/CVPR.2009.5206848
   Google Scholar
• DeVries, T., & Taylor, G. W. (2017). Dataset augmentation in feature space. Proceedings of the International Conference on Learning Representations (ICLR) (pp. 78–86). https://openreview.net/forum?id =HyaF53XYx
   Google Scholar
• Dingchao, J., Hua, Q., Jihong, Z., Jianlong, Z., & Meng-Yen, H. (2021). Aggregating multi-scale contextual features from multiple stages for semantic image segmentation. Connection Science, 33(3), 605–622. https://doi.org/10.1080/09540091.2020.1862059
   Web of Science ®Google Scholar
• Escalers, S., Fabian, J., Pardo, P., Baro, X., Gonzlez, J., Escalante, H. J., Misevic, D., Steiner, U., & Guyon, I. (2015). Chalearn looking at People 2015: Apparent age and cultural event recognition datasets and results. Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 243–251). https://doi.org/10.1109/ICCVW.2015.40
   Google Scholar
• Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 580–587). https://doi.org/10.1109/CVPR.2014.81
   Google Scholar
• Grez, A., Riveros, C., Ugarte, M., & Vansummeren, S. (2021). A formal framework for complex event recognition. ACM Transactions on Database Systems, 46(4), 1–49. https://doi.org/10.1145/3485463
   Web of Science ®Google Scholar
• Hasan, M., Paul, S., Mourikis, A. I., & Roy-Chowdhury, A. K. (2020). Context-Aware query selection for active learning in event recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(3), 554–567. https://doi.org/10.1109/TPAMI.2018.2878696
   PubMed Web of Science ®Google Scholar
• Jasper, R. R. U., Koen, E. V. D. S., Theo, G., & Arnold, W. S. (2013). Selective search for object recognition. International Journal of Computer Vision, 104(2), 154–171. https://doi.org/10.1007/s11263-013-0620-5
   Web of Science ®Google Scholar
• Kai, X., Peng, W., Xue, C., Xiangfeng, L., & Jiangqi, G. (2021). Causal event extraction using causal event element-oriented neural network. International Journal of Computational Science and Engineering, 24(6), 621–628. https://doi.org/10.1504/IJCSE.2021.119985
   Web of Science ®Google Scholar
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deepconvolutional neural networks. Communications of the Acm, 60(6), 84–90. https://doi.org/10.1145/3065386
   Web of Science ®Google Scholar
• Li, D., Wong, W. E., Wang, W., Yao, Y., & Chau, M. (2021). Detection and mitigation of label-flipping attacks in federated learning systems with KPCA and K-means. In 2021 8th International Conference on Dependable Systems and Their Applications (DSA) (pp. 551–559). https://doi.org/10.1109/DSA52907.2021.00081
   Google Scholar
• Li, J., & Li, F. (2007). What, where and who? Classifying events by scene and object recognition (2007). Proceedings of the International Conference on Computer Vision (ICCV) (pp. 1–8). https://doi.org/10.1109/ICCV.2007.4408872
   Google Scholar
• Lin, M., Chen, Q., & Yan, S. (2014). Network in network. Proceedings of the International Conference on Learning Representations (ICLR) (pp. 224–233). https://arxiv.53yu.com/abs/1312.4400
   Google Scholar
• Liu, M., Liu, X., Li, Y., Chen, X., Hauptmann, A. G., & Shan, S. (2015). Exploiting feature hierarchies with convolutional neural networks for cultural event recognition. Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCV) (pp. 274–279). https://doi.org/10.1109/ICCVW.2015.44
   Google Scholar
• Liu, X., & Huet, B. (2013). Heterogeneous features and model selection for event-based media classification. Proceedings of the ACM Conference on International Conference on Multimedia Retrieval (ICMR) (pp. 151–158). https://doi.org/10.1145/2461466.2461493
   Google Scholar
• Ouyang, L., & Hu, P. (2017). An improved similarity computation module based on basic semantics and influencing factors of concepts in domain ontology. Proceedings of the 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (pp. 2880–2885). https://doi.org/10.1109/FSKD.2017.8393238
   Google Scholar
• Pan, X., Ding, M., & Yang, M. (2022). A geometrical perspective on image style transfer with adversarial learning. IEEE Transaction on Pattern Analysis and MachineIntelligence (TPAMI), 44(1), 63–75. https://doi.org/10.1109/TPAMI.2020.3011143
   PubMed Web of Science ®Google Scholar
• Park, S., & Kwak, N. (2015). Cultural event recognition by subregion classification with convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 45–50). https://doi.org/10.1109/CVPRW.2015.7301335
   Google Scholar
• Rachmadi, R. F., Uchimura, K., & Koutaki, G. (2016). Combined convolutional neural network for event recognition. Proceedings of the Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) (pp. 85–90).
   Google Scholar
• Salvador, A., Zeppelzauer, M., Manchon-Vizuete, D., Calafell, A., & Nieto, X. G. (2015). Cultural event recognition with visual convnets and temporal models. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 36–44). https://doi.org/10.1109/CVPRW.2015.7301334
   Google Scholar
• Shaokang, C., Dezhi, H., Xinming, Y., Dun, L., & Chin-Chen, C. (2022). A hybrid parallel deep learning model for efficient intrusion detection based on metric learning. Connection Science, 34(1), 551–577. https://doi.org/10.1080/09540091.2021.2024509
   Web of Science ®Google Scholar
• Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations (ICLR). http://arxiv.org/abs/1409.1556
   Google Scholar
• Tzelepis, C., Ma, Z., Mezaris, V., Ionescu, B., Kompatsiaris, I., Boato, G., Sebe, N., & Yan, S. (2016). Event-based media processing and analysis: A survey of the literature. Image and Vision Computing, 53, 3–19. https://doi.org/10.1016/j.imavis.2016.05.005
   Web of Science ®Google Scholar
• Wang, L., Wang, Z., Guo, S., & Qiao, Y. (2015). Better exploiting OS-CNNs for better event recognition in images. Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCV) (pp. 287–294). https://doi.org/10.1109/ICCVW.2015.46
   Google Scholar
• Wang, L., Wang, Z., Qiao, Y., & Gool, L. V. (2017). Transferring deep object and scene representations for event recognition in still images. International Journal of Computer Vision, 390–409. https://doi.org/10.1007/s11263-017-1043-5
   Google Scholar
• Wang, L., Wang, Z., Qiao, Y., & Gool, L. V. (2018). Transferring deep object and scene representation. International Journal of Computer Vision, 126(2-4), 390–409. https://doi.org/10.1007/s11263-017-1043-5
   Web of Science ®Google Scholar
• Wei, Z., Wei, L., & Zhaoquan, C. (2019). High-dimensional Arnold inverse transformation for multiple images scrambling. International Journal of Computational Science and Engineering, 20(3), 362–375. https://doi.org/10.1504/IJCSE.2019.103941
   Web of Science ®Google Scholar
• Xiong, Y., Zhu, K., Lin, D., & Tang, X. (2015). Recognize complex events from static images by fusing deep channels. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1600–1609). https://doi.org/10.1109/CVPR.2015.7298768
   Google Scholar
• Yager, R. R., & Filev, D. P. (1999). Induced ordered weighted averaging operators. IEEE Transactions on Systems(Cybernetics), 29(2), 141–150. https://doi.org/10.1109/3477.752789
   Google Scholar
• Yan, J., Wei, L., Jintian, T., & Hongbo, Z. (2021). A novel data representation framework based on nonnegative manifold regularisation. Connection Science, 33(2), 136–152. https://doi.org/10.1080/09540091.2020.1772722
   Web of Science ®Google Scholar
• Yang, S., Luo, P., Loy, C. C., & Tang, X. (2016). WIDER FACE: A face detection benchmark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 5525–5533). https://doi.org/10.1109/CVPR.2016.596
   Google Scholar
• Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., & Oliva, A. (2014). Learning deep features for scene recognition using places database. Proceedings of the Conference and Workshop on Neural Information Processing Systems (NIPS) (pp. 487–495). https://proceedings.neurips.cc/paper/2014/hash/3fe94a002317b5f9259f82690aeea4cd-Abstract.html.
   Google Scholar