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| Author | Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi | ||||
| Title | Few shots are all you need: A progressive learning approach for low resource handwritten text recognition | Type | Journal Article | ||
| Year | 2022 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 160 | Issue | Pages | 43-49 | |
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| Abstract | Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | DAG; 600.121; 600.162; 602.230 | Approved | no | ||
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Admin @ si @ SFK2022 | Serial | 3736 | ||
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| Author | David Sanchez-Mendoza; David Masip; Agata Lapedriza | ||||
| Title | Emotion recognition from mid-level features | Type | Journal Article | ||
| Year | 2015 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 67 | Issue | Part 1 | Pages | 66–74 |
| Keywords | Facial expression; Emotion recognition; Action units; Computer vision | ||||
| Abstract | In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception. | ||||
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| Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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| ISSN | 0167-8655 | ISBN | Medium | ||
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| Notes | OR;MV | Approved | no | ||
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Admin @ si @ SML2015 | Serial | 2746 | ||
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| 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 | |
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| 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. |
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| Notes | HuPBA; ISE;MILAB; 600.098; 600.119 | Approved | no | ||
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Admin @ si @ TEG2016 | Serial | 2843 | ||
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| Author | Kai Wang; Joost Van de Weijer; Luis Herranz | ||||
| Title | ACAE-REMIND for online continual learning with compressed feature replay | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 150 | Issue | Pages | 122-129 | |
| Keywords | online continual learning; autoencoders; vector quantization | ||||
| Abstract | Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset. | ||||
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| Notes | LAMP; 600.147; 601.379; 600.120; 600.141 | Approved | no | ||
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Admin @ si @ WWH2021 | Serial | 3575 | ||
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| Author | M. Bressan; Jordi Vitria | ||||
| Title | Nonparametric Discriminant Analysis and Nearest Neighbor Classification | Type | Journal Article | ||
| Year | 2003 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 24 | Issue | 15 | Pages | 2743–2749 |
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| Abstract | IF: 0.809 | ||||
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| Notes | OR;MV | Approved | no | ||
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BCNPCL @ bcnpcl @ BrV2003b | Serial | 367 | ||
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| Author | Cristina Cañero; Petia Radeva | ||||
| Title | Vesselness enhancement diffusion | Type | Journal Article | ||
| Year | 2003 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 24 | Issue | 16 | Pages | 3141–3151 |
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| Abstract | IF: 0.809 | ||||
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| Notes | MILAB | Approved | no | ||
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BCNPCL @ bcnpcl @ CaR2003 | Serial | 371 | ||
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| Author | Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados | ||||
| Title | Blurred Shape Model for Binary and Grey-level Symbol Recognition | Type | Journal Article | ||
| Year | 2009 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 30 | Issue | 15 | Pages | 1424–1433 |
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| Abstract | Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance. | ||||
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| Notes | HuPBA; DAG; MILAB | Approved | no | ||
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BCNPCL @ bcnpcl @ EFP2009a | Serial | 1180 | ||
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| Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
| Title | Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes | Type | Journal Article | ||
| Year | 2009 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 30 | Issue | 3 | Pages | 285–297 |
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| Abstract | Error Correcting Output Codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework. Based on the new measure, we stress on how to design coding matrices preventing codification ambiguity and propose a new Sparse Random coding matrix with ternary distance maximization. The results on the UCI Repository and in a real speed traffic categorization problem show that when the coding design satisfies the new ternary measures, significant performance improvement is obtained independently of the decoding strategy applied. | ||||
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| Notes | MILAB;HuPBA | Approved | no | ||
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BCNPCL @ bcnpcl @ EPR2009a | Serial | 1153 | ||
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| Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
| Title | Re-coding ECOCs without retraining | Type | Journal Article | ||
| Year | 2010 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 31 | Issue | 7 | Pages | 555–562 |
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| Abstract | A standard way to deal with multi-class categorization problems is by the combination of binary classifiers in a pairwise voting procedure. Recently, this classical approach has been formalized in the Error-Correcting Output Codes (ECOC) framework. In the ECOC framework, the one-versus-one coding demonstrates to achieve higher performance than the rest of coding designs. The binary problems that we train in the one-versus-one strategy are significantly smaller than in the rest of designs, and usually easier to be learnt, taking into account the smaller overlapping between classes. However, a high percentage of the positions coded by zero of the coding matrix, which implies a high sparseness degree, does not codify meta-class membership information. In this paper, we show that using the training data we can redefine without re-training, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information helps the system to increase its generalization capability. Moreover, the new re-coding strategy is generalized to be applied over any binary code. The results over several UCI Machine Learning repository data sets and two real multi-class problems show that performance improvements can be obtained re-coding the classical one-versus-one and Sparse random designs compared to different state-of-the-art ECOC configurations. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | MILAB;HUPBA | Approved | no | ||
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BCNPCL @ bcnpcl @ EPR2010e | Serial | 1338 | ||
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| Author | David Guillamet; Jordi Vitria | ||||
| Title | Evaluation of distance metrics for recognition based on non-negative matrix factorization | Type | Journal Article | ||
| Year | 2003 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 24 | Issue | 9-10 | Pages | 1599 –1605 |
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| Abstract | IF: 0.809 | ||||
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| Notes | OR;MV | Approved | no | ||
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BCNPCL @ bcnpcl @ GuV2003b | Serial | 380 | ||
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| Author | David Guillamet; Jordi Vitria; B. Shiele | ||||
| Title | Introducing a weighted non-negative matrix factorization for image classification | Type | Journal Article | ||
| Year | 2003 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 24 | Issue | 14 | Pages | 2447–2454 |
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| Abstract | IF: 0.809 | ||||
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| Notes | OR;MV | Approved | no | ||
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BCNPCL @ bcnpcl @ GVS2003 | Serial | 382 | ||
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| Author | A. Martinez; Jordi Vitria | ||||
| Title | Learning mixture models using a genetic version of the EM algorithm. | Type | Journal Article | ||
| Year | 2000 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 21 | Issue | 8 | Pages | 759–769 |
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| Notes | OR;MV | Approved | no | ||
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BCNPCL @ bcnpcl @ MVi2000 | Serial | 335 | ||
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| Author | Xavier Otazu; Oriol Pujol | ||||
| Title | Wavelet based approach to cluster analysis. Application on low dimensional data sets | Type | Journal Article | ||
| Year | 2006 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 27 | Issue | 14 | Pages | 1590–1605 |
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| Notes | MILAB; CIC; HuPBA | Approved | no | ||
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BCNPCL @ bcnpcl @ OtP2006 | Serial | 658 | ||
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| Author | Jordi Vitria; J. Llacer | ||||
| Title | Reconstructing 3D light microscopic images using the EM algorithm | Type | Journal | ||
| Year | 1996 | Publication | Pattern Recognition Letters | Abbreviated Journal | |
| Volume | 17 | Issue | 14 | Pages | 1491–1498 |
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| Notes | OR;MV | Approved | no | ||
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BCNPCL @ bcnpcl @ ViL1996 | Serial | 74 | ||
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| Author | Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva | ||||
| Title | ROC curves and video analysis optimization in intestinal capsule endoscopy | Type | Journal Article | ||
| Year | 2006 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 27 | Issue | 8 | Pages | 875–881 |
| Keywords | ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy | ||||
| Abstract | Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. | ||||
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| Area | 800 | Expedition | Conference | ||
| Notes | MILAB;MV;SIAI | Approved | no | ||
| Call Number |
BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 | Serial | 647 | ||
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