|
Javier Varona, & Juan J. Villanueva. (1997). Neural Networks for Early Vision..
|
|
|
Javier Varona, & Juan J. Villanueva. (1997). NeuroFilters: Neural Networks for image Processing..
|
|
|
Fernando Vilariño, & Enric Marti. (2008). New didactic techniques in the EHES applying mobile technologies.
|
|
|
Antonio Lopez, W. Niessen, Joan Serrat, K. Nicolay, Bart M. Ter Haar Romeny, Juan J. Villanueva, et al. (1999). New improvements in the multiscale analysis of trabecular bone patterns..
|
|
|
Antonio Lopez, W. Niessen, Joan Serrat, K. Nicolay, Bart M. Ter Haar Romeny, Juan J. Villanueva, et al. (2000). New improvements in the multiscale analysis of trabecular bone patterns..
|
|
|
Adriana Romero, Petia Radeva, & Carlo Gatta. (2014). No more meta-parameter tuning in unsupervised sparse feature learning.
Abstract: CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well.
|
|
|
Azadeh S. Mozafari, David Vazquez, Mansour Jamzad, & Antonio Lopez. (2016). Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest.
Abstract: Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved.
Keywords: Domain Adaptation; Pedestrian detection; Random Forest
|
|
|
J.R. Serra, S. Casadei, & J.B. Subirana. (1995). Non-Cartesian Networks for Middle Level Vision..
|
|
|
David Guillamet, & Jordi Vitria. (2002). Non-negative Matrix Factorization for Face Recognition..
|
|
|
David Guillamet, & Jordi Vitria. (2001). Non-negative Matrix Factorization to Extract Part-Based Representations..
|
|
|
David Guillamet, & Jordi Vitria. (2001). Non-negative Matrix Factorization to Extract Part-Based Representations..
|
|
|
Jaume Amores, & Petia Radeva. (2003). Non-rigid Registration of Vessel Structures in IVUS Images.
|
|
|
Jose Luis Alba, A. Pujol, & Juan J. Villanueva. (2001). Novel SOM-PCA Network for Face Identification..
|
|
|
M. Gomez, J. Mauri, E. Fernandez-Nofrerias, Oriol Rodriguez-Leor, Carme Julia, Petia Radeva, et al. (2002). Nuevos Avances para la correlacion de imagenes angiograficas y de ecograia intracoronaria..
|
|
|
Ernest Valveny, & Antonio Lopez. (2003). Numeral Recognition for Quality Control of Surgical Sachets.
|
|