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Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
On the Decoding Process in Ternary Error-Correcting Output Codes |
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2010 |
Publication |
IEEE on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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32 |
Issue |
1 |
Pages  |
120–134 |
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Abstract |
A common way to model multiclass classification problems is to design a set of binary classifiers and to combine them. Error-correcting output codes (ECOC) represent a successful framework to deal with these type of problems. Recent works in the ECOC framework showed significant performance improvements by means of new problem-dependent designs based on the ternary ECOC framework. The ternary framework contains a larger set of binary problems because of the use of a ldquodo not carerdquo symbol that allows us to ignore some classes by a given classifier. However, there are no proper studies that analyze the effect of the new symbol at the decoding step. In this paper, we present a taxonomy that embeds all binary and ternary ECOC decoding strategies into four groups. We show that the zero symbol introduces two kinds of biases that require redefinition of the decoding design. A new type of decoding measure is proposed, and two novel decoding strategies are defined. We evaluate the state-of-the-art coding and decoding strategies over a set of UCI machine learning repository data sets and into a real traffic sign categorization problem. The experimental results show that, following the new decoding strategies, the performance of the ECOC design is significantly improved. |
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0162-8828 |
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MILAB;HUPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2010b |
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1277 |
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Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Title |
Hand pose aware multimodal isolated sign language recognition |
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Journal Article |
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Year |
2020 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
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80 |
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127–163 |
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Isolated hand sign language recognition from video is a challenging research area in computer vision. Some of the most important challenges in this area include dealing with hand occlusion, fast hand movement, illumination changes, or background complexity. While most of the state-of-the-art results in the field have been achieved using deep learning-based models, the previous challenges are not completely solved. In this paper, we propose a hand pose aware model for isolated hand sign language recognition using deep learning approaches from two input modalities, RGB and depth videos. Four spatial feature types: pixel-level, flow, deep hand, and hand pose features, fused from both visual modalities, are input to LSTM for temporal sign recognition. While we use Optical Flow (OF) for flow information in RGB video inputs, Scene Flow (SF) is used for depth video inputs. By including hand pose features, we show a consistent performance improvement of the sign language recognition model. To the best of our knowledge, this is the first time that this discriminant spatiotemporal features, benefiting from the hand pose estimation features and multi-modal inputs, are fused for isolated hand sign language recognition. We perform a step-by-step analysis of the impact in terms of recognition performance of the hand pose features, different combinations of the spatial features, and different recurrent models, especially LSTM and GRU. Results on four public datasets confirm that the proposed model outperforms the current state-of-the-art models on Montalbano II, MSR Daily Activity 3D, and CAD-60 datasets with a relative accuracy improvement of 1.64%, 6.5%, and 7.6%. Furthermore, our model obtains a competitive results on isoGD dataset with only 0.22% margin lower than the current state-of-the-art model. |
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HUPBA; no menciona |
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no |
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Admin @ si @ RKE2020 |
Serial |
3524 |
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Author |
Miguel Angel Bautista; Antonio Hernandez; Sergio Escalera; Laura Igual; Oriol Pujol; Josep Moya; Veronica Violant; Maria Teresa Anguera |
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Title |
A Gesture Recognition System for Detecting Behavioral Patterns of ADHD |
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Journal Article |
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2016 |
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IEEE Transactions on System, Man and Cybernetics, Part B |
Abbreviated Journal |
TSMCB |
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46 |
Issue |
1 |
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136-147 |
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Gesture Recognition; ADHD; Gaussian Mixture Models; Convex Hulls; Dynamic Time Warping; Multi-modal RGB-Depth data |
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We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context. |
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HuPBA; MILAB; |
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no |
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Admin @ si @ BHE2016 |
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2566 |
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Author |
Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon |
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Title |
Looking at People Special Issue |
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Journal Article |
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2018 |
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International Journal of Computer Vision |
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IJCV |
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126 |
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2-4 |
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141-143 |
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HUPBA; ISE; 600.119 |
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no |
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Admin @ si @ EGJ2018 |
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3093 |
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Author |
Daniel Sanchez; Miguel Angel Bautista; Sergio Escalera |
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Title |
HuPBA 8k+: Dataset and ECOC-GraphCut based Segmentation of Human Limbs |
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Journal Article |
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Year |
2015 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
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Volume |
150 |
Issue |
A |
Pages |
173–188 |
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Human limb segmentation; ECOC; Graph-Cuts |
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Abstract |
Human multi-limb segmentation in RGB images has attracted a lot of interest in the research community because of the huge amount of possible applications in fields like Human-Computer Interaction, Surveillance, eHealth, or Gaming. Nevertheless, human multi-limb segmentation is a very hard task because of the changes in appearance produced by different points of view, clothing, lighting conditions, occlusions, and number of articulations of the human body. Furthermore, this huge pose variability makes the availability of large annotated datasets difficult. In this paper, we introduce the HuPBA8k+ dataset. The dataset contains more than 8000 labeled frames at pixel precision, including more than 120000 manually labeled samples of 14 different limbs. For completeness, the dataset is also labeled at frame-level with action annotations drawn from an 11 action dictionary which includes both single person actions and person-person interactive actions. Furthermore, we also propose a two-stage approach for the segmentation of human limbs. In a first stage, human limbs are trained using cascades of classifiers to be split in a tree-structure way, which is included in an Error-Correcting Output Codes (ECOC) framework to define a body-like probability map. This map is used to obtain a binary mask of the subject by means of GMM color modelling and GraphCuts theory. In a second stage, we embed a similar tree-structure in an ECOC framework to build a more accurate set of limb-like probability maps within the segmented user mask, that are fed to a multi-label GraphCut procedure to obtain final multi-limb segmentation. The methodology is tested on the novel HuPBA8k+ dataset, showing performance improvements in comparison to state-of-the-art approaches. In addition, a baseline of standard action recognition methods for the 11 actions categories of the novel dataset is also provided. |
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HuPBA;MILAB |
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no |
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Admin @ si @ SBE2015 |
Serial |
2552 |
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