2017 |
Vasileios Charisopoulos, Petros Maragos Morphological perceptrons: Geometry and training algorithms Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10225 LNCS , 2017, ISSN: 16113349. Abstract | BibTeX | Links: [PDF] @conference{346, title = {Morphological perceptrons: Geometry and training algorithms}, author = { Vasileios Charisopoulos and Petros Maragos}, url = {http://robotics.ntua.gr/wp-content/uploads/publications/CharisopoulosMaragos_MorphologPerceptGeometryTrainAlgor_ISMM17.pdf}, doi = {10.1007/978-3-319-57240-6_1}, issn = {16113349}, year = {2017}, date = {2017-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {10225 LNCS}, pages = {3--15}, abstract = {Neural networks have traditionally relied on mostly linear models, such as the multiply-accumulate architecture of a linear perceptron that remains the dominant paradigm of neuronal computation. However, from a biological standpoint, neuron activity may as well involve inherently nonlinear and competitive operations. Mathematical morphology and minimax algebra provide the necessary background in the study of neural networks made up from these kinds of nonlinear units. This paper deals with such a model, called the morphological perceptron. We study some of its geometrical properties and introduce a training algorithm for binary classification. We point out the relationship between morphological classifiers and the recent field of tropical geometry, which enables us to obtain a precise bound on the number of linear regions of the maxout unit, a popular choice for deep neural networks introduced recently. Finally, we present some relevant numerical results.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Neural networks have traditionally relied on mostly linear models, such as the multiply-accumulate architecture of a linear perceptron that remains the dominant paradigm of neuronal computation. However, from a biological standpoint, neuron activity may as well involve inherently nonlinear and competitive operations. Mathematical morphology and minimax algebra provide the necessary background in the study of neural networks made up from these kinds of nonlinear units. This paper deals with such a model, called the morphological perceptron. We study some of its geometrical properties and introduce a training algorithm for binary classification. We point out the relationship between morphological classifiers and the recent field of tropical geometry, which enables us to obtain a precise bound on the number of linear regions of the maxout unit, a popular choice for deep neural networks introduced recently. Finally, we present some relevant numerical results. |
2011 |
N Malandrakis, A Potamianos, G Evangelopoulos, A Zlatintsi A Supervised Approach to Movie Emotion Tracking Conference Proc. {IEEE} Int'l Conf. Acous., Speech, and Signal Processing, Prague, Czech Republic, 2011. Abstract | BibTeX | Links: [PDF] @conference{MPE+11, title = {A Supervised Approach to Movie Emotion Tracking}, author = {N Malandrakis and A Potamianos and G Evangelopoulos and A Zlatintsi}, url = {http://robotics.ntua.gr/wp-content/publications/Malandrakis+_movie_emotion_ICASSP11.pdf}, year = {2011}, date = {2011-05-01}, booktitle = {Proc. {IEEE} Int'l Conf. Acous., Speech, and Signal Processing}, address = {Prague, Czech Republic}, abstract = {In this paper, we present experiments on continuous time, continuous scale affective movie content recognition (emotion tracking). A major obstacle for emotion research has been the lack of appropriately annotated databases, limiting the potential for supervised algorithms. To that end we develop and present a database of movie affect, annotated in continuous time, on a continuous valence-arousal scale. Supervised learning methods are proposed to model the continuous affective response using hidden Markov Models (independent) in each dimension. These models classify each video frame into one of seven discrete categories (in each dimension); the discrete-valued curves are then converted to continuous values via spline interpolation. A variety of audio-visual features are investigated and an optimal feature set is selected. The potential of the method is experimentally verified on twelve 30-minute movie clips with good precision at a macroscopic level.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } In this paper, we present experiments on continuous time, continuous scale affective movie content recognition (emotion tracking). A major obstacle for emotion research has been the lack of appropriately annotated databases, limiting the potential for supervised algorithms. To that end we develop and present a database of movie affect, annotated in continuous time, on a continuous valence-arousal scale. Supervised learning methods are proposed to model the continuous affective response using hidden Markov Models (independent) in each dimension. These models classify each video frame into one of seven discrete categories (in each dimension); the discrete-valued curves are then converted to continuous values via spline interpolation. A variety of audio-visual features are investigated and an optimal feature set is selected. The potential of the method is experimentally verified on twelve 30-minute movie clips with good precision at a macroscopic level. |
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