|
Records |
Links |
|
Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
A Flexible Outlier Detector Based on a Topology Given by Graph Communities |
Type |
Journal Article |
|
Year |
2022 |
Publication |
Big Data Research |
Abbreviated Journal |
BDR |
|
|
Volume |
29 |
Issue |
|
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
100332 |
|
|
Keywords |
Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors |
|
|
Abstract |
Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors.
This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings. |
|
|
Address |
August 28, 2022 |
|
|
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; IAM; 600.140; 600.121; 600.139; 600.145; 600.159 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RBG2022a |
Serial |
3718 |
|
Permanent link to this record |
|
|
|
|
Author |
Andres Mafla; Ruben Tito; Sounak Dey; Lluis Gomez; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
|
|
Title |
Real-time Lexicon-free Scene Text Retrieval |
Type |
Journal Article |
|
Year |
2021 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
|
Volume |
110 |
Issue |
|
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
107656 |
|
|
Keywords |
|
|
|
Abstract |
In this work, we address the task of scene text retrieval: given a text query, the system returns all images containing the queried text. The proposed model uses a single shot CNN architecture that predicts bounding boxes and builds a compact representation of spotted words. In this way, this problem can be modeled as a nearest neighbor search of the textual representation of a query over the outputs of the CNN collected from the totality of an image database. Our experiments demonstrate that the proposed model outperforms previous state-of-the-art, while offering a significant increase in processing speed and unmatched expressiveness with samples never seen at training time. Several experiments to assess the generalization capability of the model are conducted in a multilingual dataset, as well as an application of real-time text spotting in videos. |
|
|
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; 600.121; 600.129; 601.338 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MTD2021 |
Serial |
3493 |
|
Permanent link to this record |
|
|
|
|
Author |
Lei Kang; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture |
Type |
Journal Article |
|
Year |
2021 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
|
Volume |
112 |
Issue |
|
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
107790 |
|
|
Keywords |
|
|
|
Abstract |
Sequence-to-sequence models have recently become very popular for tackling
handwritten word recognition problems. However, how to effectively integrate an external language model into such recognizer is still a challenging
problem. The main challenge faced when training a language model is to
deal with the language model corpus which is usually different to the one
used for training the handwritten word recognition system. Thus, the bias
between both word corpora leads to incorrectness on the transcriptions, providing similar or even worse performances on the recognition task. In this
work, we introduce Candidate Fusion, a novel way to integrate an external
language model to a sequence-to-sequence architecture. Moreover, it provides suggestions from an external language knowledge, as a new input to
the sequence-to-sequence recognizer. Hence, Candidate Fusion provides two
improvements. On the one hand, the sequence-to-sequence recognizer has
the flexibility not only to combine the information from itself and the language model, but also to choose the importance of the information provided
by the language model. On the other hand, the external language model
has the ability to adapt itself to the training corpus and even learn the
most commonly errors produced from the recognizer. Finally, by conducting
comprehensive experiments, the Candidate Fusion proves to outperform the
state-of-the-art language models for handwritten word recognition tasks. |
|
|
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; 600.140; 601.302; 601.312; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ KRV2021 |
Serial |
3343 |
|
Permanent link to this record |
|
|
|
|
Author |
Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Learning graph edit distance by graph neural networks |
Type |
Journal Article |
|
Year |
2021 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
|
Volume |
120 |
Issue |
|
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
108132 |
|
|
Keywords |
|
|
|
Abstract |
The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words i.e. keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark 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 |
DAG; 600.140; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RFL2021 |
Serial |
3611 |
|
Permanent link to this record |
|
|
|
|
Author |
S.K. Jemni; Mohamed Ali Souibgui; Yousri Kessentini; Alicia Fornes |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
|
|
Title |
Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement |
Type |
Journal Article |
|
Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
|
Volume |
123 |
Issue |
|
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
108370 |
|
|
Keywords |
|
|
|
Abstract |
Handwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on. These artifacts raise many readability issues for current Handwritten Text Recognition (HTR) algorithms and severely devalue their efficiency. In this paper, we propose an end to end architecture based on Generative Adversarial Networks (GANs) to recover the degraded documents into a and form. Unlike the most well-known document binarization methods, which try to improve the visual quality of the degraded document, the proposed architecture integrates a handwritten text recognizer that promotes the generated document image to be more readable. To the best of our knowledge, this is the first work to use the text information while binarizing handwritten documents. Extensive experiments conducted on degraded Arabic and Latin handwritten documents demonstrate the usefulness of integrating the recognizer within the GAN architecture, which improves both the visual quality and the readability of the degraded document images. Moreover, we outperform the state of the art in H-DIBCO challenges, after fine tuning our pre-trained model with synthetically degraded Latin handwritten images, on this task. |
|
|
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; 600.124; 600.121; 602.230 |
Approved |
no |
|
|
Call Number |
Admin @ si @ JSK2022 |
Serial |
3613 |
|
Permanent link to this record |