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24th International Conference on Pattern Recognition (ICPR)

作者： Rill-Garcia, Rodrigo Villasenor-Pineda, Luis Reyes-Meza, Veronica Jair Escalante, Hugo Inst Nacl Astrofis Opt & Electr Puebla 72840 Mexico Univ Autonoma Tlaxcala Ctr Tlaxcala Biol Conducta Tlaxcala Mexico

ISBN: (纸本)9783030057923;9783030057916

Deception detection -identifying when someone is trying to cause someone else to believe something that is not true- is a hard task for humans. The task is even harder for automatic approaches, that must deal with additional problems like the lack of enough labeled data. In this context, transfer learning in the form of cross-domain classification is a task that aims to leverage labeled data from certain domains for which labeled data is available to others for which data is scarce. This paper presents a study on the suitability of linguistic features for crossdomain deception detection on multimodal data. Specifically, we aim to learn models for deception detection across different domains of written texts (one modality) and apply the new knowledge to unrelated topics transcribed from spoken statements (another modality). Experimental results reveal that by using LIWC and POS n-grams we reach a inmodality accuracy of 69.42%, as well as an AUC ROC of 0.7153. When doing transfer learning, we achieve an accuracy of 63.64% and get an AUC ROC of 0.6351.

关键词： Linguistic analysis Cross-domain classification multimodal data analysis Deception detection

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FaRoC: Fast and Robust Supervised Canonical Correlation analysis for multimodal Omics

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第4期48卷 1229-1241页

作者： Mandal, Ankita Maji, Pradipta Indian Stat Inst Biomed Imaging & Bioinformat Lab Machine Intelligence Unit Kolkata 700108 India

One of the main problems associated with high dimensional multimodal real life data sets is how to extract relevant and significant features. In this regard, a fast and robust feature extraction algorithm, termed as FaRoC, is proposed, integrating judiciously the merits of canonical correlation analysis (CCA) and rough sets. The proposed method extracts new features sequentially from two multidimensional data sets by maximizing their relevance with respect to class label and significance with respect to already-extracted features. To generate canonical variables sequentially, an analytical formulation is introduced to establish the relation between regularization parameters and CCA. The formulation enables the proposed method to extract required number of correlated features sequentially with lesser computational cost as compared to existing methods. To compute both significance and relevance measures of a feature, the concept of hypercuboid equivalence partition matrix of rough hypercuboid approach is used. It also provides an efficient way to find optimum regularization parameters employed in CCA. The efficacy of the proposed FaRoC algorithm, along with a comparison with other existing methods, is extensively established on several real life data sets.

关键词： Canonical correlation analysis (CCA) classification feature extraction multimodal data analysis rough sets

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EAT - The ICMI 2018 Eating analysis and Tracking Challenge 18

EAT - The ICMI 2018 Eating Analysis and Tracking Challenge

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20th ACM International Conference on multimodal Interaction (ICMI)

作者： Hantke, Simone Schmitt, Maximilian Tzirakis, Panagiotis Schuller, Bjorn Tech Univ Munich Machine Intelligence & Signal Proc Grp Munich Germany Univ Augsburg ZDB Chair Embedded Intelligence Hlth Care & Wellb Augsburg Germany Imperial Coll London GLAM London England

ISBN: (纸本)9781450356923

The multimodal recognition of eating condition - whether a person is eating or not - and if yes, which food type, is a new research domain in the area of speech and video processing that has many promising applications for future multimodal interfaces such as adapting speech recognition or lip reading systems to different eating conditions. We herein describe the ICMI 2018 EATING analysis AND TRACKING (EAT) CHALLENGE and address - for the first time in research competitions under well-defined conditions - new classification tasks in the area of user data analysis, namely audiovisual classifications of user eating conditions. We define three Sub-Challenges based on classification tasks in which participants are encouraged to use speech and/or video recordings of the audiovisual tHEARu-EAT database. In this paper, we describe the dataset, the Sub-Challenges, their conditions, and the baseline feature extraction and performance measures as provided to the participants.

关键词： Challenge multimodal data analysis Eating Condition Human Behaviour

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A novel cross-modal hashing algorithm based on multimodal deep learning

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Science China(Information Sciences) 2017年第9期60卷 50-63页

作者： Wen QU Daling WANG Shi FENG Yifei ZHANG Ge YU School of Information Science and Engineering Northeastern University Key Laboratory of Medical Image Computing

With the growing popularity of multimodal data on the Web, cross-modal retrieval on large-scale multimedia databases has become an important research topic. Cross-modal retrieval methods based on hashing assume that there is a latent space shared by multimodal features. To model the relationship among heterogeneous data, most existing methods embed the data into a joint abstraction space by linear projections. However,these approaches are sensitive to noise in the data and are unable to make use of unlabeled data and multimodal data with missing values in real-world applications. To address these challenges, we proposed a novel multimodal deep-learning-based hash(MDLH) algorithm. In particular, MDLH uses a deep neural network to encode heterogeneous features into a compact common representation and learns the hash functions based on the common representation. The parameters of the whole model are fine-tuned in a supervised training *** on two standard datasets show that the method achieves more effective results than other methods in cross-modal retrieval.

关键词： hashing cross-modal retrieval cross-modal hashing multimodal data analysis deep learning

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multimodal Omics data Integration Using Max Relevance-Max Significance Criterion

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2017年第8期64卷 1841-1851页

作者： Maji, Pradipta Mandal, Ankita Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata 700108 India

Objective: This paper presents a novel supervised regularized canonical correlation analysis, termed as CuRSaR, to extract relevant and significant features from multimodal high dimensional omics datasets. Methods: The proposed method extracts a new set of features from two multidimensional datasets by maximizing the relevance of extracted features with respect to sample categories and significance among them. It integrates judiciously the merits of regularized canonical correlation analysis (RCCA) and rough hypercuboid approach. An analytical formulation, based on spectral decomposition, is introduced to establish the relation between canonical correlation analysis (CCA) and RCCA. The concept of hypercuboid equivalence partition matrix of rough hypercuboid is used to compute both relevance and significance of a feature. Significance: The analytical formulation makes the computational complexity of the proposed algorithm significantly lower than existing methods. The equivalence partition matrix offers an efficient way to find optimum regularization parameters employed in CCA. Results: The superiority of the proposed algorithm over other existing methods, in terms of computational complexity and classification accuracy, is established extensively on real life data.

关键词： Canonical correlation analysis (CCA) classification feature extraction multimodal data analysis rough sets

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Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features

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MEDICAL IMAGE analysis 2016年 34卷 30-41页

作者： Hor, Soheil Moradi, Mehdi Univ British Columbia Vancouver BC Canada IBM Almaden Res Ctr San Jose CA 95120 USA

Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. (C) 2016 Elsevier B.V. All rights reserved.

关键词： multimodal data analysis Incomplete data analysis Decision forest

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Challenges in multimodal gesture recognition

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JOURNAL OF MACHINE LEARNING RESEARCH 2016年第1期17卷 2549-2602页

作者： Escalera, Sergio Athitsos, Vassilis Guyon, Isabelle Comp Vis Ctr UAB Barcelona Spain Univ Barcelona E-08007 Barcelona Spain Univ Texas Arlington Arlington TX 76019 USA ChaLearn Berkeley CA USA

This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the Kinect (TM) revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.

关键词： Gesture Recognition Time Series analysis multimodal data analysis Computer Vision Pattern Recognition Wearable sensors Infrared Cameras Kinect (TM)

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Challenges in multimodal gesture recognition

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Sergio Escalera Vassilis Athitsos Isabelle Guyon Google MPI for Intelligent Systems Computer Vision Center UAB and University of Barcelona University of Texas ChaLearn Berkeley California

This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the Kinect™ revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.

关键词： Kinect™ computer vision gesture recognition infrared cameras multimodal data analysis pattern recognition time series analysis wearable sensors

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Human computing and machine understanding of human behavior: A survey

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International Workshop on Artificial Intelligence for Human Computing

作者： Pantic, Maja Pentland, Alex Nijholt, Anton Huang, Thomas S. Imperial Coll London Dept Comp London England MIT Media Lab Cambridge MA 02139 USA Univ Twente EEMCS Enschede Netherlands Univ Illinois Beckman Inst Champaign IL USA

ISBN: (纸本)9783540723462

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, humanlike interactive functions including understanding and emulating certain human behaviors such as affecti0ve and social signaling. This article discusses how far are we from enabling computers to understand human behavior.

关键词： human sensing human behavior understanding multimodal data analysis affective computing socially-aware computing

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Dyslexia:: the possible benefit of multimodal integration of fMRI- and EEG-data

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JOURNAL OF NEURAL TRANSMISSION 2004年第7期111卷 951-969页

作者： Grünling, C Ligges, M Huonker, R Klingert, M Mentzel, HJ Rzanny, R Kaiser, WA Witte, H Blanz, B Univ Jena Dept Child & Adolescent Psychiat D-07743 Jena Germany Univ Jena Dept Neurol Biomagnet Ctr D-07743 Jena Germany Univ Jena Inst Diagnost & Intervent Radiol D-07743 Jena Germany Univ Jena Inst Med Stat Comp Sci & Documentat D-07743 Jena Germany

Biological research about dyslexia has been conducted using various neuroimaging methods like functional Magnetic Resonance Imaging (fMRI) or Electroencephalography (EEG). Since language functions are characterized by both distributed network activities and speed of processing within milliseconds, high temporal as well as high spatial resolution of activation profiles are of interest: "where" can dyslexia specific activations be detected and "when" do language processes start to diverge between dyslexics and controls? Due to the network character of language processing, fMRI-constrained distributed source models based on EEG-data were computed for multimodal data integration. First single-case results show that this method could be a promising approach for the understanding of a repeatedly described experimental finding for dyslexia like that of an overactivation in inferior frontal language areas. multimodal data analysis for the subjects presented here could probably demonstrate that inferior frontal overactivations are the consequence of a phonological deficit and could represent ongoing articulation processes used to solve phonologically challenging tasks.

关键词： fMRI ERP multimodal data analysis dyslexia phonological deficit hypothesis language

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