| |
Records |
Links |
|
Author  |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol |
|
| |
Title |
Minimal Design of Error-Correcting Output Codes |
Type |
Journal Article |
| |
Year |
2011 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
33 |
Issue |
6 |
Pages |
693-702 |
|
| |
Keywords |
Multi-class classification; Error-correcting output codes; Ensemble of classifiers |
|
| |
Abstract |
IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Elsevier |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0167-8655 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB; OR;HuPBA;MV |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BEB2011a |
Serial |
1800 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Miguel Reyes; Albert Clapes; Jose Ramirez; Juan R Revilla; Sergio Escalera |
|
| |
Title |
Automatic Digital Biometry Analysis based on Depth Maps |
Type |
Journal Article |
| |
Year |
2013 |
Publication |
Computers in Industry |
Abbreviated Journal |
COMPUTIND |
|
| |
Volume |
64 |
Issue |
9 |
Pages |
1316-1325 |
|
| |
Keywords |
Multi-modal data fusion; Depth maps; Posture analysis; Anthropometric data; Musculo-skeletal disorders; Gesture analysis |
|
| |
Abstract |
World Health Organization estimates that 80% of the world population is affected by back-related disorders during his life. Current practices to analyze musculo-skeletal disorders (MSDs) are expensive, subjective, and invasive. In this work, we propose a tool for static body posture analysis and dynamic range of movement estimation of the skeleton joints based on 3D anthropometric information from multi-modal data. Given a set of keypoints, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matched, and accurate measurements about posture and spinal curvature are computed. Given a set of joints, range of movement measurements is also obtained. Moreover, gesture recognition based on joint movements is performed to look for the correctness in the development of physical exercises. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent MSDs, as well as tracking the posture evolution of patients in rehabilitation treatments. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Elsevier |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HuPBA;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RCR2013 |
Serial |
2252 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund |
|
| |
Title |
Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
80 |
Issue |
|
Pages |
208–215 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper proposes a technique for RGB-D scene segmentation using Multi-class
Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset.
The approach shows that simple multi-modal features with the power of the MMSSL
paradigm can achieve better performance than state of the art results on the same dataset. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HuPBA; ISE;MILAB; 600.098; 600.119 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ TEG2016 |
Serial |
2843 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mohamed Ilyes Lakhal; Hakan Çevikalp; Sergio Escalera; Ferda Ofli |
|
| |
Title |
Recurrent Neural Networks for Remote Sensing Image Classification |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
IET Computer Vision |
Abbreviated Journal |
IETCV |
|
| |
Volume |
12 |
Issue |
7 |
Pages |
1040 - 1045 |
|
| |
Keywords |
|
|
| |
Abstract |
Automatically classifying an image has been a central problem in computer vision for decades. A plethora of models has been proposed, from handcrafted feature solutions to more sophisticated approaches such as deep learning. The authors address the problem of remote sensing image classification, which is an important problem to many real world applications. They introduce a novel deep recurrent architecture that incorporates high-level feature descriptors to tackle this challenging problem. Their solution is based on the general encoder–decoder framework. To the best of the authors’ knowledge, this is the first study to use a recurrent network structure on this task. The experimental results show that the proposed framework outperforms the previous works in the three datasets widely used in the literature. They have achieved a state-of-the-art accuracy rate of 97.29% on the UC Merced dataset. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HUPBA; no proj;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ LÇE2018 |
Serial |
3119 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad |
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HuPBA; no proj;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BGE2017 |
Serial |
3014 |
|
| Permanent link to this record |
