@proceedings {128, title = {A Nonparametric Bayesian Approach Toward Stacked Convolutional Independent Component Analysis}, journal = {International Conference on Computer Vision}, year = {2015}, abstract = {
Unsupervised feature learning algorithms based on con- volutional formulations of independent components analysis (ICA) have been demonstrated to yield state-of-the-art results in several action recognition benchmarks. However, existing approaches do not allow for the number of latent components (features) to be automatically inferred from the data in an unsupervised manner. This is a significant dis- advantage of the state-of-the-art, as it results in consider- able burden imposed on researchers and practitioners, who must resort to tedious cross-validation procedures to obtain the optimal number of latent features. To resolve these issues, in this paper we introduce a convolutional nonpara- metric Bayesian sparse ICA architecture for overcomplete feature learning from high-dimensional data. Our method utilizes an Indian buffet process prior to facilitate inference of the appropriate number of latent features under a hybrid variational inference algorithm, scalable to massive datasets. As we show, our model can be naturally used to obtain deep unsupervised hierarchical feature extractors, by greedily stacking successive model layers, similar to existing approaches. In addition, inference for this model is completely heuristics-free; thus, it obviates the need of te- dious parameter tuning, which is a major challenge most deep learning approaches are faced with. We evaluate our method on several action recognition benchmarks, and ex- hibit its advantages over the state-of-the-art.
}, author = {Sotirios P. Chatzis and Dimitrios I. Kosmopoulos} } @proceedings {126, title = {A Non-Stationary Infinite Partially Observable Markov Decision Process}, journal = {International Conference on Artificial Neural Networks}, year = {2014}, abstract = {Partially Observable Markov Decision Processes (POMDPs) have been met with great success in planning domains where agents must balance actions that provide knowledge and actions that provide reward. Recently, nonparametric Bayesian methods have been successfully applied to POMDPs to obviate the need of a priori knowledge of the size of the state space, allowing to assume that the number of visited states may grow as the agent explores its environment. These approaches rely on the assumption that the agent{\textquoteright}s environment remains stationary; however, in real-world scenarios the environment may change over time. In this work, we aim to address this inadequacy by introducing a dynamic nonparametric Bayesian POMDP model that both allows for automatic inference of the (distributional) representations of POMDP states, and for capturing non-stationarity in the modeled environments. Formulation of our method is based on imposition of a suitable dynamic hierarchical Dirichlet process (dHDP) prior over state transitions. We derive efficient algorithms for model inference and action planning and evaluate it on several benchmark tasks.}, author = {Sotirios P. Chatzis and Dimitrios I. Kosmopoulos} } @article {38, title = {A Conditional Random Field-Based Model for Joint Sequence Segmentation and Classification}, journal = {Pattern Recognition}, volume = {46}, year = {2013}, month = {06/2013}, pages = {1569-1578}, author = {Sotirios P. Chatzis and D. Kosmopoulos and P. Doliotis} } @article {14, title = {Visual Workflow Recognition Using a Variational Bayesian Treatment of Multistream Fused Hidden Markov Models}, journal = {Circuits and Systems for Video Technology, IEEE Transactions on}, volume = {22}, year = {2012}, month = {July}, pages = {1076 -1086}, keywords = {active learning-based visual workflow recognition, Bayes methods, classification variance, data labeling, hidden Markov models, image sequences, learned model, learning (artificial intelligence), maximum a posteriori training, maximum information gain method, maximum likelihood training, MFHMM parameters, model variance, multistream fused hidden Markov models, overfitting issues, point estimate-based methods, posterior distribution, training methods, unlabeled data, variational Bayesian treatment, video streaming, video surveillance}, issn = {1051-8215}, doi = {10.1109/TCSVT.2012.2189795}, author = {Sotirios P. Chatzis and D. Kosmopoulos} } @article {24, title = {A variational Bayesian methodology for hidden Markov models utilizing Student{\textquoteright}s-t mixtures}, journal = {Pattern Recognition}, volume = {44}, year = {2011}, pages = {295 - 306}, keywords = {Violence detection}, issn = {0031-3203}, doi = {10.1016/j.patcog.2010.09.001}, author = {Sotirios P. Chatzis and D. Kosmopoulos} } @article {5, title = {Robust Visual Behavior Recognition}, journal = {Signal Processing Magazine, IEEE}, volume = {27}, year = {2010}, month = {Sep.}, pages = {34-45}, issn = {1053-5888}, author = {D. Kosmopoulos and Sotirios P. Chatzis} } @article {3, title = {Robust Sequential Data Modeling Using an Outlier Tolerant Hidden Markov Model}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {31}, year = {2009}, pages = {1657-1669}, issn = {0162-8828}, author = {Sotirios P. Chatzis and D. Kosmopoulos and Varvarigou, T.A.} } @article {27, title = {Vision-based production of personalized video}, journal = {Signal Processing: Image Communication}, volume = {24}, year = {2009}, pages = {158 - 176}, keywords = {Automated content production, Human identification, tracking}, issn = {0923-5965}, doi = {10.1016/j.image.2008.12.010}, author = {D.I. Kosmopoulos and A. Doulamis and Makris, A. and N. Doulamis and Sotirios P. Chatzis and S.E. Middleton} } @proceedings {15, title = {A robust approach towards sequential data modeling and its application in automatic gesture recognition}, journal = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on}, year = {2008}, month = {April}, pages = {1937-1940}, keywords = {automatic gesture recognition, data representation, finite Gaussian mixture model, Gaussian distribution, gesture recognition, hidden Markov model, hidden Markov models, hidden state distribution, maximum likelihood estimation, maximum likelihood framework, parameter estimation, sequential data modeling, student-t mixture model, time series, time series modeling}, doi = {10.1109/ICASSP.2008.4518015}, author = {Sotirios P. Chatzis and D. Kosmopoulos and Varvarigou, T.A.} } @article {12, title = {Signal Modeling and Classification Using a Robust Latent Space Model Based on t Distributions}, journal = {Signal Processing, IEEE Transactions on}, volume = {56}, year = {2008}, month = {March}, pages = {949 -963}, keywords = {Bayes methods, Bayesian inference, factor analysis modeling, inference mechanisms, robust latent space model, signal classification, signal modeling, signal processing, statistical distributions, t distribution, variational inference algorithm}, issn = {1053-587X}, doi = {10.1109/TSP.2007.907912}, author = {Sotirios P. Chatzis and D. Kosmopoulos and Varvarigou, T.A.} }