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Author Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari edit  doi
openurl 
  Title Audio-Visual Emotion Recognition in Video Clips Type Journal Article
  Year 2019 Publication IEEE Transactions on Affective Computing Abbreviated Journal TAC  
  Volume 10 Issue 1 Pages 60-75  
  Keywords  
  Abstract This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks’ geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE’05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases.  
  Address 1 Jan.-March 2019  
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  Area Expedition Conference  
  Notes HUPBA; 602.143; 602.133;MILAB Approved no  
  Call Number Admin @ si @ NMN2017 Serial 3011  
Permanent link to this record
 

 
Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad edit  url
openurl 
  Title Locality Regularized Group Sparse Coding for Action Recognition Type Journal Article
  Year 2017 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
  Volume 158 Issue Pages 106-114  
  Keywords Bag of words; Feature encoding; Locality constrained coding; Group sparse coding; Alternating direction method of multipliers; Action recognition  
  Abstract Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets.  
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  Notes HuPBA; no proj;MILAB Approved no  
  Call Number Admin @ si @ BGE2017 Serial 3014  
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Author Miguel Angel Bautista; Oriol Pujol; Fernando De la Torre; Sergio Escalera edit   pdf
url  doi
openurl 
  Title Error-Correcting Factorization Type Journal Article
  Year 2018 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 40 Issue Pages 2388-2401  
  Keywords  
  Abstract Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi- class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches.  
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  Series Volume Series Issue Edition  
  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA; no menciona;MILAB Approved no  
  Call Number Admin @ si @ BPT2018 Serial 3015  
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Author Huamin Ren; Nattiya Kanhabua; Andreas Mogelmose; Weifeng Liu; Kaustubh Kulkarni; Sergio Escalera; Xavier Baro; Thomas B. Moeslund edit  url
doi  openurl
  Title Back-dropout Transfer Learning for Action Recognition Type Journal Article
  Year 2018 Publication IET Computer Vision Abbreviated Journal IETCV  
  Volume 12 Issue 4 Pages 484-491  
  Keywords Learning (artificial intelligence); Pattern Recognition  
  Abstract Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate.  
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  Notes HUPBA; no proj;MV;OR;MILAB Approved no  
  Call Number Admin @ si @ RKM2018 Serial 3071  
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Author Mark Philip Philipsen; Jacob Velling Dueholm; Anders Jorgensen; Sergio Escalera; Thomas B. Moeslund edit  doi
openurl 
  Title Organ Segmentation in Poultry Viscera Using RGB-D Type Journal Article
  Year 2018 Publication Sensors Abbreviated Journal SENS  
  Volume 18 Issue 1 Pages 117  
  Keywords semantic segmentation; RGB-D; random forest; conditional random field; 2D; 3D; CNN  
  Abstract We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11% is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28% using only basic 2D image features.  
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  Notes HUPBA; no proj;MILAB Approved no  
  Call Number Admin @ si @ PVJ2018 Serial 3072  
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