Machine Learning for Media Data Analysis
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1 27 OKTOBER 2010 Machine Learning for Media Data Analysis Assistant Machine Learning and Computer Vision Aarhus University,
2 Overview Media Data Analysis applications Image analysis/recognition/segmentation Video analysis/segmentation Face recognition/verification and affective computing Human action recognition/localization/segmentation Domain-specific image/video analysis Recent contributions Graph-based analysis/recognition/clustering Max-margin Classification Discriminant Learning Kernel-based learning Multi-view/modal Data Analysis Neural Network (Deep Learning) acceleration Data-driven (Deep) Architecture Learning Extension to applications of other domains
3 27 OKTOBER 2010 Media Data Analysis applications
4 Human Face Analysis Face recognition A Iosifidis, A Tefas and I Pitas, Kernel Reference Discriminant Analysis, Pattern Recognition Letters, 2014 A Iosifidis, A Tefas and I Pitas, "Class-specific Reference Discriminant Analysis with application in Human Behavior Analysis", IEEE Transactions on Human-Machine Systems, 2015
5 Human Face Analysis Face verification Face identification A Iosifidis and M Gabbouj, Class-Specific Kernel Discriminant Analysis revisited: further analysis and extensions, IEEE Transactions on Cybernetics, 2017 A Iosifidis and M Gabbouj, "Scaling up Class-Specific Kernel Discriminant Analysis for largescale Face Verification", IEEE Transactions on Information Forensics and Security, 2016
6 Human Face Analysis Affective Computing (Facial expression recognition) MOBISERV FP A Iosifidis, A Tefas and I Pitas, Class-specific Reference Discriminant Analysis with application in Human Behavior Analysis, IEEE Transactions on Human-Machine Systems, 2015
7 Human Face Analysis Visual Voice Activity Detection Assign the correct face to the observed voice We were the first to perform VVAD in the wild! F Patrona, A Iosifidis, A Tefas, N Nikolaidis and I Pitas, Visual Voice Activity Detection in the Wild, IEEE Transactions on Multimedia, 2016
8 Human Face Analysis Face Sketch recognition G Cao, A Iosifidis and M Gabbouj, "Multi-modal Subspace Learning with Dropout regularization for Cross-modal Recognition and Retrieval ", IPTA 2016 (Best Student Paper Award)
9 Image Analysis Salient object segmentation Unsupervised (generic) case C Aytekin, A Iosifidis and M Gabbouj, "Probabilistic Saliency Estimation", Pattern Recognition, 2018
10 Image Analysis Salient object segmentation Supervised (User-directed case) CKN CKN CKN CKN H EQCUT CKN CKN C Aytekin, A Iosifidis, S Kiranyaz and M Gabbouj, Learning Graph Affinities for Spectral Graphbased Salient Object Detection, Pattern Recognition, 2017
11 Human Action Recognition How to combine action observations from various views/cameras Restricted application scenario Movie production i3dpost FP6 A Iosifidis, A Tefas and I Pitas, "View-invariant action recognition based on Artificial Neural Networks", IEEE Transactions on Neural Networks and Learning Systems, 2012 A Iosifidis, A Tefas, N Nikolaidis and I Pitas, "Multi-view Human Movement Recognition based on Fuzzy Distances and Linear Discriminant Analysis", Computer Vision and Image Understanding, 2012
12 Human Action Recognition How to combine action observations from various views/cameras Restricted application scenario Movie production Localization of people and classification of their actions i3dpost FP6 A Iosifidis, A Tefas and I Pitas, "View-invariant action recognition based on Artificial Neural Networks", IEEE Transactions on Neural Networks and Learning Systems, 2012 A Iosifidis, A Tefas, N Nikolaidis and I Pitas, "Multi-view Human Movement Recognition based on Fuzzy Distances and Linear Discriminant Analysis", Computer Vision and Image Understanding, 2012
13 Human Action Recognition Human action recognition for assisted living of the elderly MOBISERV FP7 A Iosifidis, E Marami, A Tefas, I Pitas and K Lyroudia, The MOBISERV-AIIA Eating and Drinking multi-view database for vision-based assisted living, J-IHMSP, 2015
14 Human Action Recognition What can we do for complex actions in complex/cluttered scenes?
15 Human Action Recognition What can we do for complex actions in complex/cluttered scenes? We focus on Space-Time Interest Points (STIPs) and follow the similar approaches A Iosifidis, A Tefas and I Pitas, "Discriminant Bag of Words based Representation for Human Action Recognition", Pattern Recognition Letters, 2014 A Iosifidis, A Tefas and I Pitas, "Distance-based Human Action Recognition using optimized class representations", Neurocomputing, 2015
16 Human Action Recognition When enriched visual information is available Stereo Cameras 3DTVS FP7 Left channel Right channel Disparity map Disparity zone 2 Disparity zone 1 A Iosifidis, A Tefas and I Pitas, Human Action Recognition in Stereoscopic Videos based on Bag of Features and Disparity Pyramids, EUSIPCO, 2014
17 Human Action Recognition When enriched visual information is available Stereo Cameras Original DT-based description Disparity-enhanced DTbased description I Mademlis, A Iosifidis, A Tefas, N Nikolaidis and I Pitas, Exploiting Stereoscopic Disparity for Augmenting Human Activity Recognition Performance, Multimedia Tools and Applications, 2015
18 Video analysis Object tracking A Iosifidis, SG Mouroutsos and A Gasteratos, A hybrid static/active video surveillance system, International Journal of Optomechatronics, 2011
19 Video analysis Object detection/recognition Scene analysis M Waris, A Iosifidis and M Gabbouj, CNN-based Edge Filtering for Object Proposals, Neurocomputing, 2017
20 Multi-view/modal Data analysis Image and Text (I2T and T2I) Retrieval G Cao, A Iosifidis, K Chen and M Gabbouj, "Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval", IEEE Transactions on Cybernetics, 2018
21 Domain-specific Media Data Analysis Classification of Aquatic Macroinvertebrates (bugs in lakes) J Arje, S Karkkainen, K Meissner, A Iosifidis, T Ince, M Gabbouj and S Kiranyaz, The effect of automated taxa identification errors on biological indices, Expert Systems with Applications, 2017 J Raitoharju, E Riabchenko, K Meissner, I Ahmad, A Iosifidis, M Gabbouj and S Kiranyaz, Data Enrichment in Fine-Grained Classification of Aquatic Macroinvertebrates, ICPR, 2016
22 27 OKTOBER 2010 Recent Contributions
23 Discriminant Learning Class-specific kernel Discriminant Analysis Data represented as vectors Vector to vector transformation/mapping Find a data mapping that maximizes discrimination of the class of interest from the rest of the world Intra-class scatter S i Out-of-class scatter S p
24 Discriminant Learning Traditional CSKDA: 1 Data mapping to the feature space: 2 Application of the linear projection in S i = Φ L i Φ T S p = Φ L p Φ T W T S i W = A T Φ T Φ L i Φ T ΦA = A T K L i KA W T S p W = A T Φ T Φ L p Φ T ΦA = A T K L p KA A is calculated by applying eigenanalysis to the matrix (K L p K) -1 (K L i K) stability issues!
25 Discriminant Learning We showed that the standard CSKDA solution is equivalent to: using data-independent targets T! Benefits Much stable and fast solutions can be calculated Approximation schemes are readily available and extremely efficient/effective Incremental/Decremental solutions are available Hierarchical (deep) models for class-specific learning are now possible A Iosifidis and M Gabbouj, Class-Specific Kernel Discriminant Analysis revisited: further analysis and extensions, IEEE Transactions on Cybernetics, 2017
26 Discriminant Learning Deep Class-specific Discriminant Analysis model: G Cao, A Iosifidis and M Gabbouj, Neural Class-Specific Projections for Face Verification, IET Biometrics, 2017
27 Classification Max-margin based classification Find the decision hyperplane discriminating the classes with maximum margin Theoretical guarantees for generalization error One (global) solution 2/ w w T x + b = -1 w T x + b = 0 w T x + b = 1
28 Classification Binary classification: st: y i Є {-1,1} are the binary labels Multi-class classification: st:
29 Classification In order to increase class discrimination Discriminant data mapping Max-margin classification
30 Classification Discriminant max-margin based classification We proved that these two processing steps can be applied at once! Max-margin to a discriminant (kernel) space Multi-class classification: Discriminant Term st: Joint optimization of K decision functions {w k, b k }, k=1,, K A Iosifidis and M Gabbouj, Multi-class Support Vector Machine Classifiers using Intrinsic and Penalty Graphs, Pattern Recognition, 2016
31 Probability-based Visual Saliency We modeled visual saliency (for salient object segmentation) using probabilistic The probability of an image region (pixel/super-pixel) to be salient is obtained by: Properties Global optimum solution Generic framework for visual saliency: Diffusion methods are special cases of PSE PSE optimally refines the solution of QCut (SoA saliency estimation method) Now Saliency Estimation can be modelled as an One-Class Classification Problem C Aytekin, A Iosifidis and M Gabbouj, Probabilistic Saliency Estimation, Pattern Recognition, 2018
32 Supervised Visual Saliency End-to-end learning for visual saliency that: Exploits successive learnable feature transformations Optimizes the end-to-end model using global information CKN CKN CKN CKN H EQCUT CKN CKN C Aytekin, A Iosifidis, S Kiranyaz and M Gabbouj, Learning Graph Affinities for Spectral Graph-based Salient Object Detection, Pattern Recognition, 2018
33 Multi-view/modal Data Analysis Generalized Multi-view Embedding We showed that most multi-view embedding methods can be modeled using the Raylegh Quotient G Cao, A Iosifidis, K Chen and M Gabbouj, Generalized Multi-view Embedding, IEEE Transactions on Cybernetics, 2018
34 Multi-view/modal Data Analysis Generalized Multi-view Embedding We showed that most multi-view embedding methods can be modeled using the Raylegh Quotient Based on this observation, we proposed a new MvLDA criterion incorporating inter-view and intra-view variance criteria G Cao, A Iosifidis, K Chen and M Gabbouj, Generalized Multi-view Embedding, IEEE Transactions on Cybernetics, 2018
35 Multi-view/modal Data Analysis Generalized Multi-view Embedding G Cao, A Iosifidis, K Chen and M Gabbouj, Generalized Multi-view Embedding, IEEE Transactions on Cybernetics, 2018
36 Neural Networks (Deep Learning) Basic idea of neural network-based learning Data-driven fine-tuning the parameters of a network to regress inputs to targets using: User-defined number of layers User-defined activation functions (always the same for the entire network, except the last one) User-defined neuron pooling operator (always the same for all neurons) User-defined neuron nodal operator (always the same for all neurons)
37 Neural Networks (Deep Learning) We proposed a Progressive Operational Feedforward Neural network learning approach Data-driven network s architecture Data-driven network s parameters tuning S Kiranyaz, T Ince, A Iosifidis and M Gabbouj, Progressive Operational Preceptrons, Neurocomputing, 2017
38 Convolutional Neural Networks (Deep Learning) CNN architecture: Convolutional layers Multilayer Perceptron (vector) layers
39 Convolutional Neural Networks (Deep Learning) CNN architecture: Convolutional layers Multilayer Perceptron (vector) layers CL-1 CL-2 MLP-1,2
40 Convolutional Neural Networks (Deep Learning) CNN architecture: Convolutional layers Multilayer Perceptron (vector) layers Real CNN architecture: CLs are tensors!
41 Convolutional Neural Networks (Deep Learning) We proposed a tensor-based CNN filters modeling that can lead to: Lower number of network parameters (reduced memory footprint) Faster classification (reduced computational cost) DT Thanh, A Iosifidis and M Gabbouj, Improving Efficiency in Convolutional Neural Network with Multilinear Filters, arxiv 2017
42 Convolutional Neural Networks (Deep Learning) We proposed a tensor-based CNN filters modeling that can lead to: Lower number of network parameters (reduced memory footprint) Faster classification (reduced computational cost) DT Thanh, A Iosifidis and M Gabbouj, Improving Efficiency in Convolutional Neural Network with Multilinear Filters, arxiv 2017
43 27 OKTOBER 2010 Extension to applications of other domains
44 Other applications Stock prediction in financial markets A Tsantekidis, N Passalis, A Tefas, J Kanniainen, M Gabbouj and A Iosifidis, Using Deep Learning to Detect Price Change Indications in Financial Markets, EUSIPCO, 2017 N Passalis, A Tsantekidis, A Tefas, J Kanniainen, M Gabbouj and A Iosifidis, Time-series Classification using Neural Bag-of-Features, EUSIPCO, 2017
45 Other applications University Ranking, Safety/hazard Assessment, Green Spaces, etc
46 Thank you for your attention!
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