| Records |
| Author |
Ivan Huerta; Marco Pedersoli; Jordi Gonzalez; Alberto Sanfeliu |
| Title |
Combining where and what in change detection for unsupervised foreground learning in surveillance |
Type |
Journal Article |
| Year |
2015 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
48 |
Issue |
3 |
Pages |
709-719 |
Keywords  |
Object detection; Unsupervised learning; Motion segmentation; Latent variables; Support vector machine; Multiple appearance models; Video surveillance |
| Abstract |
Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art. |
| 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 |
ISE; 600.063; 600.078 |
Approved |
no |
| Call Number |
Admin @ si @ HPG2015 |
Serial |
2589 |
| Permanent link to this record |
| |
|
| |
| Author |
Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta |
| Title |
Structure-preserving smoothing of biomedical images |
Type |
Journal Article |
| Year |
2011 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
44 |
Issue |
9 |
Pages |
1842-1851 |
| Keywords |
Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography |
| Abstract |
Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. |
| 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 |
0031-3203 |
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
IAM; ADAS |
Approved |
no |
| Call Number |
IAM @ iam @ GHB2011 |
Serial |
1526 |
| Permanent link to this record |
| |
|
| |
| Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa |
| Title |
Median Graphs: A Genetic Approach based on New Theoretical Properties |
Type |
Journal Article |
| Year |
2009 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
42 |
Issue |
9 |
Pages |
2003–2012 |
| Keywords |
Median graph; Genetic search; Maximum common subgraph; Graph matching; Structural pattern recognition |
| Abstract |
Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present two major contributions. On one side, and from a theoretical point of view, we show new theoretical properties of the median graph. On the other side, using these new properties, we present a new approximate algorithm based on the genetic search, that improves the computation of the median graph. Finally, we perform a set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity. With these results, we show how the concept of the median graph can be used in real applications and leaves the box of the only-theoretical concepts, demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs. |
| 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 |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ FVS2009b |
Serial |
1167 |
| Permanent link to this record |
| |
|
| |
| Author |
Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
| Title |
Efficient segmentation-free keyword spotting in historical document collections |
Type |
Journal Article |
| Year |
2015 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
48 |
Issue |
2 |
Pages |
545–555 |
| Keywords |
Historical documents; Keyword spotting; Segmentation-free; Dense SIFT features; Latent semantic analysis; Product quantization |
| Abstract |
In this paper we present an efficient segmentation-free word spotting method, applied in the context of historical document collections, that follows the query-by-example paradigm. We use a patch-based framework where local patches are described by a bag-of-visual-words model powered by SIFT descriptors. By projecting the patch descriptors to a topic space with the latent semantic analysis technique and compressing the descriptors with the product quantization method, we are able to efficiently index the document information both in terms of memory and time. The proposed method is evaluated using four different collections of historical documents achieving good performances on both handwritten and typewritten scenarios. The yielded performances outperform the recent state-of-the-art keyword spotting approaches. |
| 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 |
DAG; ADAS; 600.076; 600.077; 600.061; 601.223; 602.006; 600.055 |
Approved |
no |
| Call Number |
Admin @ si @ RAT2015a |
Serial |
2544 |
| Permanent link to this record |
| |
|
| |
| Author |
Marçal Rusiñol; Josep Llados |
| Title |
Boosting the Handwritten Word Spotting Experience by Including the User in the Loop |
Type |
Journal Article |
| Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
47 |
Issue |
3 |
Pages |
1063–1072 |
| Keywords |
Handwritten word spotting; Query by example; Relevance feedback; Query fusion; Multidimensional scaling |
| Abstract |
In this paper, we study the effect of taking the user into account in a query-by-example handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and two baseline word spotting approaches both based on the bag-of-visual-words model. We finally present two alternative ways of presenting the results to the user that might be more attractive and suitable to the user's needs than the classic ranked list. |
| 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 |
0031-3203 |
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
DAG; 600.045; 600.061; 600.077 |
Approved |
no |
| Call Number |
Admin @ si @ RuL2013 |
Serial |
2343 |
| Permanent link to this record |
| |
|
| |
| Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa; K. Riesen; Horst Bunke |
| Title |
Generalized Median Graph Computation by Means of Graph Embedding in Vector Spaces |
Type |
Journal Article |
| Year |
2010 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
43 |
Issue |
4 |
Pages |
1642–1655 |
| Keywords |
Graph matching; Weighted mean of graphs; Median graph; Graph embedding; Vector spaces |
| Abstract |
The median graph has been presented as a useful tool to represent a set of graphs. Nevertheless its computation is very complex and the existing algorithms are restricted to use limited amount of data. In this paper we propose a new approach for the computation of the median graph based on graph embedding. Graphs are embedded into a vector space and the median is computed in the vector domain. We have designed a procedure based on the weighted mean of a pair of graphs to go from the vector domain back to the graph domain in order to obtain a final approximation of the median graph. Experiments on three different databases containing large graphs show that we succeed to compute good approximations of the median graph. We have also applied the median graph to perform some basic classification tasks achieving reasonable good results. These experiments on real data open the door to the application of the median graph to a number of more complex machine learning algorithms where a representative of a set of graphs is needed. |
| 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 |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ FVS2010 |
Serial |
1294 |
| Permanent link to this record |
| |
|
| |
| Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
| Title |
A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding |
Type |
Journal Article |
| Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
46 |
Issue |
10 |
Pages |
2830-2839 |
| Keywords |
Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification |
| Abstract |
Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques. |
| 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 |
0031-3203 |
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
HuPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ BGE2013a |
Serial |
2247 |
| Permanent link to this record |
| |
|
| |
| Author |
Volkmar Frinken; Andreas Fischer; Markus Baumgartner; Horst Bunke |
| Title |
Keyword spotting for self-training of BLSTM NN based handwriting recognition systems |
Type |
Journal Article |
| Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
47 |
Issue |
3 |
Pages |
1073-1082 |
| Keywords |
Document retrieval; Keyword spotting; Handwriting recognition; Neural networks; Semi-supervised learning |
| Abstract |
The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. |
| 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 |
DAG; 600.077; 602.101 |
Approved |
no |
| Call Number |
Admin @ si @ FFB2014 |
Serial |
2297 |
| Permanent link to this record |
| |
|
| |
| Author |
Juan Ignacio Toledo; Manuel Carbonell; Alicia Fornes; Josep Llados |
| Title |
Information Extraction from Historical Handwritten Document Images with a Context-aware Neural Model |
Type |
Journal Article |
| Year |
2019 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
86 |
Issue |
|
Pages |
27-36 |
| Keywords |
Document image analysis; Handwritten documents; Named entity recognition; Deep neural networks |
| Abstract |
Many historical manuscripts that hold trustworthy memories of the past societies contain information organized in a structured layout (e.g. census, birth or marriage records). The precious information stored in these documents cannot be effectively used nor accessed without costly annotation efforts. The transcription driven by the semantic categories of words is crucial for the subsequent access. In this paper we describe an approach to extract information from structured historical handwritten text images and build a knowledge representation for the extraction of meaning out of historical data. The method extracts information, such as named entities, without the need of an intermediate transcription step, thanks to the incorporation of context information through language models. Our system has two variants, the first one is based on bigrams, whereas the second one is based on recurrent neural networks. Concretely, our second architecture integrates a Convolutional Neural Network to model visual information from word images together with a Bidirecitonal Long Short Term Memory network to model the relation among the words. This integrated sequential approach is able to extract more information than just the semantic category (e.g. a semantic category can be associated to a person in a record). Our system is generic, it deals with out-of-vocabulary words by design, and it can be applied to structured handwritten texts from different domains. The method has been validated with the ICDAR IEHHR competition protocol, outperforming the existing approaches. |
| 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 |
DAG; 600.097; 601.311; 603.057; 600.084; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ TCF2019 |
Serial |
3166 |
| Permanent link to this record |
| |
|
| |
| Author |
Francesco Ciompi; Oriol Pujol; Petia Radeva |
| Title |
ECOC-DRF: Discriminative random fields based on error correcting output codes |
Type |
Journal Article |
| Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
47 |
Issue |
6 |
Pages |
2193-2204 |
| Keywords |
Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models |
| Abstract |
We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments. |
| 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 |
LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ CPR2014b |
Serial |
2470 |
| Permanent link to this record |
| |
|
| |
| Author |
Meysam Madadi; Hugo Bertiche; Sergio Escalera |
| Title |
SMPLR: Deep learning based SMPL reverse for 3D human pose and shape recovery |
Type |
Journal Article |
| Year |
2020 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
106 |
Issue |
|
Pages |
107472 |
| Keywords |
Deep learning; 3D Human pose; Body shape; SMPL; Denoising autoencoder; Volumetric stack hourglass |
| Abstract |
In this paper we propose to embed SMPL within a deep-based model to accurately estimate 3D pose and shape from a still RGB image. We use CNN-based 3D joint predictions as an intermediate representation to regress SMPL pose and shape parameters. Later, 3D joints are reconstructed again in the SMPL output. This module can be seen as an autoencoder where the encoder is a deep neural network and the decoder is SMPL model. We refer to this as SMPL reverse (SMPLR). By implementing SMPLR as an encoder-decoder we avoid the need of complex constraints on pose and shape. Furthermore, given that in-the-wild datasets usually lack accurate 3D annotations, it is desirable to lift 2D joints to 3D without pairing 3D annotations with RGB images. Therefore, we also propose a denoising autoencoder (DAE) module between CNN and SMPLR, able to lift 2D joints to 3D and partially recover from structured error. We evaluate our method on SURREAL and Human3.6M datasets, showing improvement over SMPL-based state-of-the-art alternatives by about 4 and 12 mm, respectively. |
| 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 |
Approved |
no |
| Call Number |
Admin @ si @ MBE2020 |
Serial |
3439 |
| Permanent link to this record |
| |
|
| |
| Author |
Daniel Ponsa; Antonio Lopez |
| Title |
Variance reduction techniques in particle-based visual contour Tracking |
Type |
Journal Article |
| Year |
2009 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
42 |
Issue |
11 |
Pages |
2372–2391 |
| Keywords |
Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling |
| Abstract |
This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done. |
| 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 |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ PoL2009a |
Serial |
1168 |
| Permanent link to this record |
| |
|
| |
| Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
| Title |
Towards Automatic Polyp Detection with a Polyp Appearance Model |
Type |
Journal Article |
| Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
45 |
Issue |
9 |
Pages |
3166-3182 |
| Keywords |
Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot |
| Abstract |
This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. |
| 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 |
0031-3203 |
ISBN |
|
Medium |
|
| Area |
800 |
Expedition |
|
Conference |
IbPRIA |
| Notes |
MV;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ BSV2012; IAM @ iam |
Serial |
1997 |
| Permanent link to this record |
| |
|
| |
| Author |
Pau Rodriguez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez |
| Title |
Age and gender recognition in the wild with deep attention |
Type |
Journal Article |
| Year |
2017 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
72 |
Issue |
|
Pages |
563-571 |
| Keywords |
Age recognition; Gender recognition; Deep neural networks; Attention mechanisms |
| Abstract |
Face analysis in images in the wild still pose a challenge for automatic age and gender recognition tasks, mainly due to their high variability in resolution, deformation, and occlusion. Although the performance has highly increased thanks to Convolutional Neural Networks (CNNs), it is still far from optimal when compared to other image recognition tasks, mainly because of the high sensitiveness of CNNs to facial variations. In this paper, inspired by biology and the recent success of attention mechanisms on visual question answering and fine-grained recognition, we propose a novel feedforward attention mechanism that is able to discover the most informative and reliable parts of a given face for improving age and gender classification. In particular, given a downsampled facial image, the proposed model is trained based on a novel end-to-end learning framework to extract the most discriminative patches from the original high-resolution image. Experimental validation on the standard Adience, Images of Groups, and MORPH II benchmarks show that including attention mechanisms enhances the performance of CNNs in terms of robustness and accuracy. |
| 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 |
ISE; 600.098; 602.133; 600.119 |
Approved |
no |
| Call Number |
Admin @ si @ RCG2017b |
Serial |
2962 |
| Permanent link to this record |
| |
|
| |
| Author |
Oriol Pujol; Sergio Escalera; Petia Radeva |
| Title |
An Incremental Node Embedding Technique for Error Correcting Output Codes |
Type |
Journal |
| Year |
2008 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
41 |
Issue |
2 |
Pages |
713–725 |
| Keywords |
|
| Abstract |
|
| 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 |
MILAB;HuPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ PER2008 |
Serial |
942 |
| Permanent link to this record |
