Publications

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Aytekin, C., Iosifidis, A., Kiranyaz, S. & Gabbouj, M. (2016). Learning Graph Affinities for Spectral Graph-based Salient Object Detection. Pattern Recognition, 64, 159-167. https://doi.org/10.1016/j.patcog.2016.11.005

Aytekin, C., Kiranyaz, S., Iosifidis, A. & Gabbouj, M. (2016). Recent advances in salient object detection -- Towards object recognition in big media data. Futura – Big Data, 35(2), 80-92. https://tutcris.tut.fi/portal/en/publications/recent-advances-in-salient-object-detection--towards-object-recognition-in-big-media-data(1e2959cf-8622-4243-b78b-6eb03f34a0ce).html

Aytekin, C., Iosifidis, A. & Gabbouj, M. (2017). Probabilistic Saliency Estimation. Pattern Recognition, 74, 359-372. https://doi.org/10.1016/j.patcog.2017.09.023

Aytekin, C., Iosifidis, A., Kiranyaz, S. & Gabbouj, M. (2016). Salient Object Segmentation based on Linearly Combined Affinity Graphs. I Pattern Recognition (ICPR), 2016 23rd International Conference on (s. 3769-3774). IEEE. https://doi.org/10.1109/ICPR.2016.7900221

Bajovic, D., Bakhtiarnia, A., Bravos, G., Brutti, A., Burkhardt, F., Cauchi, D., Chazapis, A., Cianco, C., Dall'Asen, N., Delic, V., Dimou, C., Djokic, D., Escobar-Molero, A., Esterle, L., Eyben, F., Farella, E., Festi, T., Geromitsos, A., Giakoumakis, G. ... Zammit, J. (2021). MARVEL: Multimodal Extreme Scale Data Analytics for Smart Cities Environments. I 2021 International Balkan Conference on Communications and Networking, BalkanCom 2021 (s. 143-147). IEEE. https://doi.org/10.1109/BalkanCom53780.2021.9593258

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2021). Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning. I 2021 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533875

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2021). Multi-Exit Vision Transformer for Dynamic Inference. I The 32nd British Machine Vision Conference https://www.semanticscholar.org/paper/Multi-Exit-Vision-Transformer-for-Dynamic-Inference-Bakhtiarnia-Zhang/26f61039857423c0aad2c44f0e7769cbb61246c9

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2022). Single-layer vision transformers for more accurate early exits with less overhead. Neural Networks, 153, 461-473. https://doi.org/10.1016/j.neunet.2022.06.038

Böttjer, T., Ørnskov Rønsch, G., Gonçalves Gomes, C. Â., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2021). Data-Driven Identification of Remaining Useful Life for Plastic Ijenction Moulds. I A-L. Andersen, R. Andersen, D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems: Proceedings of the Changeable, Agile, Reconﬁgurable and Virtual Production Conference and the World Mass Customization & Personalization Conference (s. 431-439). Springer. Lecture Notes in Mechanical Engineering https://www.springerprofessional.de/en/data-driven-identification-of-remaining-useful-life-for-plastic-/19816878

Böttjer, T., Ørnskov Rønsch, G., Gomes, C., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2021). Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds. I A-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems: Proceedings of the Changeable, Agile, Reconﬁgurable and Virtual Production Conference and the World Mass Customization & Personalization Conference (s. 431-439). Springer. Lecture Notes in Mechanical Engineering https://doi.org/10.1007/978-3-030-90700-6_49

Cao, G., Iosifidis, A., Chen, K. & Gabbouj, M. (2018). Generalized Multi-View Embedding for Visual Recognition and Cross-Modal Retrieval. IEEE Transactions on Cybernetics, 48(9), 2542-2555. https://doi.org/10.1109/TCYB.2017.2742705

Cao, G., Iosifidis, A. & Gabbouj, M. (2017). Multi-view Nonparametric Discriminant Analysis for Image Retrieval and Recognition. IEEE Signal Processing Letters, 24(10), 1537-1541. https://doi.org/10.1109/LSP.2017.2748392

Cao, G., Iosifidis, A. & Gabbouj, M. (2018). Neural class-specific regression for face verification. IET Biometrics, 7(1), 63-70. https://doi.org/10.1049/iet-bmt.2017.0081

Cao, G., Waris, M. A., Iosifidis, A. & Gabbouj, M. (2016). Multi-modal Subspace Learning with Dropout regularization for Cross-modal Recognition and Retrieval. I Image Processing Theory Tools and Applications (IPTA), 2016 6th International Conference on IEEE. https://doi.org/10.1109/IPTA.2016.7821032

Cao, G., Iosifidis, A., Chen, K. & Gabbouj, M. (2016). Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval. arXiv, 1-14 (+ 3). https://arxiv.org/pdf/1605.09696.pdf

Cao, G., Iosifidis, A., Gabbouj, M., Raghavan, V. & Gottumukkala, R. (2021). Deep Multi-view Learning to Rank. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1426-1438. [8845659]. https://doi.org/10.1109/TKDE.2019.2942590

Chumachenko, K., Mannisto, A., Iosifidis, A. & Raitoharju, J. (2020). Machine Learning Based Analysis of Finnish World War II Photographers. IEEE Access, 8, 144184-144196. [9159591]. https://doi.org/10.1109/ACCESS.2020.3014458

Chumachenko, K., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). Speed-up and Multi-view Extensions to Subclass Discriminant Analysis. Pattern Recognition, 111, [107660]. https://doi.org/10.1016/j.patcog.2020.107660

Chumachenko, K., Iosifidis, A. & Gabbouj, M. (2021). Robust fast subclass discriminant analysis. I 2020 28th European Signal Processing Conference (EUSIPCO) (s. 1397-1401). [9287557] IEEE. https://doi.org/10.23919/Eusipco47968.2020.9287557

Chumachenko, K., Raitoharju, J., Gabbouj, M. & Iosifidis, A. (2020). Incremental Fast Subclass Discriminant Analysis. I 2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings (s. 1771-1775). [9191328] IEEE. Proceedings - International Conference on Image Processing, ICIP Bind 2020-October https://doi.org/10.1109/ICIP40778.2020.9191328

Chumachenko, K., Iosifidis, A. & Gabbouj, M. (2022). Feedforward Neural Networks Initialization based on Discriminant Learning. Neural Networks, 146, 220-229. https://doi.org/10.1016/j.neunet.2021.11.020

Chumachenko, K., Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). Rethinking compactness in deep neural networks. Paper præsenteret ved International Conference on Machine Learning Workshop on Overparameterization. https://drive.google.com/file/d/1pu5xJMPvKuGFeVCcylEvLz00uLK3UN9I/view

Feng, H., Gomes, C., Thule, C., Lausdahl, K., Iosifidis, A. & Larsen, P. G. (2021). Introduction to Digital Twin Engineering. I C. R. Martin, M. J. Blas & A. I. Psijas (red.), 2021 Annual Modeling and Simulation Conference (ANNSIM) (s. 1-12). IEEE. https://doi.org/10.23919/ANNSIM52504.2021.9552135

Feng, H., Gomes, C., Iosifidis, A. & Larsen, P. G. (2021). Model-based monitoring and state estimation for digital twins: The Kalman filter. I C. R. Martin, M. J. Blas & A. I. Psijas (red.), Annual Modeling and Simulation Conference (ANNSIM 2021) (s. 1-2). Simulation Series Bind 53 Nr. 2

Gautam, C., Tiwari, A. & Iosifidis, A. (2019). Minimum Variance-Embedded Multi-layer Kernel Ridge Regression for One-class Classification. I S. Sundaram (red.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (s. 389-396). [8628692] IEEE. https://doi.org/10.1109/SSCI.2018.8628692

Gautam, C., Tiwari, A., Mishra, P. K., Suresh, S., Iosifidis, A. & Tanveer, M. (2021). Graph-Embedded Multi-Layer Kernel Ridge Regression for One-Class Classification. Cognitive Computation, 13(2), 552-569. https://doi.org/10.1007/s12559-020-09804-7

Hansen, O. L. P., Svenning, J. C., Olsen, K., Dupont, S., Garner, B. H., Iosifidis, A., Price, B. W. & Høye, T. T. (2020). Species-level image classification with convolutional neural network enables insect identification from habitus images. Ecology and Evolution, 10(2), 737-747. https://doi.org/10.1002/ece3.5921

Hedegaard Morsing, L., Sheikh-Omar, O. A. & Iosifidis, A. (2021). Supervised Domain Adaptation using Graph Embedding. Paper præsenteret ved 25th International Conference on Pattern Recognition, Milan, Italien. https://arxiv.org/abs/2003.04063

Hedegaard Morsing, L., Sheikh-Omar, O. A. & Iosifidis, A. (2021). Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol. IEEE Transactions on Image Processing, 30, 8619-8631. https://doi.org/10.1109/TIP.2021.3118978

Heidari, N. & Iosifidis, A. (2021). Progressive Graph Convolutional Networks for Semi-Supervised Node Classification. IEEE Access, 9, 81957-81968. https://doi.org/10.1109/ACCESS.2021.3085905

Heidari, N. & Iosifidis, A. (2021). Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition. I Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition (ICPR) (s. 7907-7914). IEEE. https://doi.org/10.1109/ICPR48806.2021.9412091

Heidari, N. & Iosifidis, A. (2021). Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition. I ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (s. 3220-3224). IEEE. https://doi.org/10.1109/ICASSP39728.2021.9413860

Heidari, N. & Iosifidis, A. (2021). On the spatial attention in spatio-temporal graph convolutional networks for skeleton-based human action recognition. I IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings IEEE. https://doi.org/10.1109/IJCNN52387.2021.9534440

Hvilshøj, F., Iosifidis, A. & Assent, I. (2021). ECINN: Efficient Counterfactuals from Invertible Neural Networks. I 32nd British Machine Vision Conference 2021, BMVC 2021, Virtual https://arxiv.org/abs/2103.13701

Høye, T. T., Ärje, J., Bjerge, K., Hansen, O. L. P., Iosifidis, A., Leese, F., Mann, H. M. R., Meissner, K., Melvad, C. & Raitoharju, J. (2021). Deep learning and computer vision will transform entomology. Proceedings of the National Academy of Sciences of the United States of America, 118(2), [e2002545117]. https://doi.org/10.1073/pnas.2002545117

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Class-specific Reference Discriminant Analysis with application in Human Behavior Analysis. IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 45(3), 315-326. https://doi.org/10.1109/THMS.2014.2379274

Iosifidis, A. (2015). Extreme Learning Machine based Supervised Subspace Learning. Neurocomputing, 167, 158-164. https://doi.org/10.1016/j.neucom.2015.04.083

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Sparse Extreme Learning Machine classifier exploiting Intrinsic Graphs. Pattern Recognition Letters, 65, 192-196. https://doi.org/10.1016/j.patrec.2015.07.036

Iosifidis, A., Tefas, A. & Pitas, I. (2015). DropELM: Fast Neural Network Regularization with Dropout and DropConnect. Neurocomputing, 162, 57-66. https://doi.org/10.1016/j.neucom.2015.04.006

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Distance-based Human Action Recognition using optimized class representations. Neurocomputing, 161, 47-55. https://doi.org/10.1016/j.neucom.2014.10.088

Iosifidis, A., Tefas, A. & Pitas, I. (2014). On the Kernel Extreme Learning Machine Classifier. Pattern Recognition Letters, 54, 11-17. https://doi.org/10.1016/j.patrec.2014.12.003

Iosifidis, A., Marami, E., Tefas, A., Pitas, I. & Lyroudia, K. (2015). The MOBISERV-AIIA Eating and Drinking multi-view database for vision-based assisted living. Journal of Information Hiding and Multimedia Signal Processing, 6(2), 254-273. http://bit.kuas.edu.tw/~jihmsp/2015/vol6/JIH-MSP-2015-02-008.pdf

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Human Action Recognition based on Multi-view Regularized Extreme Learning Machine. International Journal on Artificial Intelligence Tools, 24(5), [1540020]. https://doi.org/10.1142/S0218213015400205

Iosifidis, A., Tefas, A. & Pitas, I. (2014). Kernel Reference Discriminant Analysis. Pattern Recognition Letters, 49, 85-91. https://doi.org/10.1016/j.patrec.2014.06.013

Iosifidis, A., Tefas, A. & Pitas, I. (2014). Discriminant Bag of Words based Representation for Human Action Recognition. Pattern Recognition Letters, 49, 185-192. https://doi.org/10.1016/j.patrec.2014.07.011

Iosifidis, A., Tefas, A. & Pitas, I. (2014). Regularized Extreme Learning Machine for Multi-view Semi-supervised Action Recognition. Neurocomputing, 145, 250-262. https://doi.org/10.1016/j.neucom.2014.05.036

Iosifidis, A., Tefas, A. & Pitas, I. (2012). Multidimensional Sequence Classification based on Fuzzy Distances and Discriminant Analysis. IEEE Transactions on Knowledge and Data Engineering, 25(11), 2564-2575. https://doi.org/10.1109/TKDE.2012.223

Iosifidis, A., Tefas, A. & Pitas, I. (2013). Minimum Class Variance Extreme Learning Machine for Human Action Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 23(11), 1968-1979. https://doi.org/10.1109/TCSVT.2013.2269774

Iosifidis, A., Tefas, A. & Pitas, I. (2012). Dynamic action recognition based on Dynemes and Extreme Learning Machine. Pattern Recognition Letters, 34(15), 1890-1898. https://doi.org/10.1016/j.patrec.2012.10.019

Iosifidis, A., Tefas, A. & Pitas, I. (2013). Learning sparse representations for view-independent human action recognition based on fuzzy distances. Neurocomputing, 121, 344-353. https://doi.org/10.1016/j.neucom.2013.05.021

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Revideret 06.09.2022

Alexandros Iosifidis