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Carlo Gatta; Eloi Puertas; Oriol Pujol |
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Title |
Multi-Scale Stacked Sequential Learning |
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Journal Article |
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Year |
2011 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
44 |
Issue |
10-11 |
Pages |
2414-2416 |
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Keywords |
Stacked sequential learning; Multiscale; Multiresolution; Contextual classification |
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Abstract |
One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions. |
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Elsevier |
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MILAB;HuPBA |
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no |
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Admin @ si @ GPP2011 |
Serial |
1802 |
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Author |
Antonio Hernandez; Sergio Escalera; Stan Sclaroff |
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Title |
Poselet-basedContextual Rescoring for Human Pose Estimation via Pictorial Structures |
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Journal Article |
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Year |
2016 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
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Volume |
118 |
Issue |
1 |
Pages |
49–64 |
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Keywords |
Contextual rescoring; Poselets; Human pose estimation |
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In this paper we propose a contextual rescoring method for predicting the position of body parts in a human pose estimation framework. A set of poselets is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body part hypotheses. A method is proposed for the automatic discovery of a compact subset of poselets that covers the different poses in a set of validation images while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for each body joint detection, given its relationship to detections of other body joints and mid-level parts in the image. This new score is incorporated in the pictorial structure model as an additional unary potential, following the recent work of Pishchulin et al. Experiments on two benchmarks show comparable results to Pishchulin et al. while reducing the size of the mid-level representation by an order of magnitude, reducing the execution time by 68 % accordingly. |
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Springer US |
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0920-5691 |
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HuPBA;MILAB; |
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Admin @ si @ HES2016 |
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2719 |
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Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera |
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Title |
Human Limb Segmentation in Depth Maps based on Spatio-Temporal Graph Cuts Optimization |
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2012 |
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Journal of Ambient Intelligence and Smart Environments |
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JAISE |
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4 |
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6 |
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535-546 |
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Multi-modal vision processing; Random Forest; Graph-cuts; multi-label segmentation; human body segmentation |
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We present a framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α−β swap Graph-cuts algorithm. Moreover, depth values of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches. |
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1876-1364 |
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MILAB;HuPBA |
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Admin @ si @ HZM2012a |
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2006 |
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Author |
Antonio Hernandez; Miguel Reyes; Victor Ponce; Sergio Escalera |
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Title |
GrabCut-Based Human Segmentation in Video Sequences |
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Journal Article |
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Year |
2012 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
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Volume |
12 |
Issue |
11 |
Pages |
15376-15393 |
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Keywords |
segmentation; human pose recovery; GrabCut; GraphCut; Active Appearance Models; Conditional Random Field |
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In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. Spatial information is included by Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, full face and pose recovery is obtained by combining human segmentation with Active Appearance Models and Conditional Random Fields. Results over public datasets and in a new Human Limb dataset show a robust segmentation and recovery of both face and pose using the presented methodology. |
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HuPBA;MILAB |
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no |
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Admin @ si @ HRP2012 |
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2147 |
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Author |
Antonio Hernandez; Miguel Angel Bautista; Xavier Perez Sala; Victor Ponce; Sergio Escalera; Xavier Baro; Oriol Pujol; Cecilio Angulo |
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Title |
Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
50 |
Issue |
1 |
Pages |
112-121 |
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Keywords |
RGB-D; Bag-of-Words; Dynamic Time Warping; Human Gesture Recognition |
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Abstract |
PATREC5825
We present a methodology to address the problem of human gesture segmentation and recognition in video and depth image sequences. A Bag-of-Visual-and-Depth-Words (BoVDW) model is introduced as an extension of the Bag-of-Visual-Words (BoVW) model. State-of-the-art RGB and depth features, including a newly proposed depth descriptor, are analysed and combined in a late fusion form. The method is integrated in a Human Gesture Recognition pipeline, together with a novel probability-based Dynamic Time Warping (PDTW) algorithm which is used to perform prior segmentation of idle gestures. The proposed DTW variant uses samples of the same gesture category to build a Gaussian Mixture Model driven probabilistic model of that gesture class. Results of the whole Human Gesture Recognition pipeline in a public data set show better performance in comparison to both standard BoVW model and DTW approach. |
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HuPBA;MV; 605.203 |
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Admin @ si @ HBP2014 |
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2353 |
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