|
Records |
Links |
|
Author |
Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
|
|
Title |
Product graph-based higher order contextual similarities for inexact subgraph matching |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
|
Volume |
76 |
Issue |
|
Pages |
596-611 |
|
|
Keywords |
|
|
|
Abstract |
Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem. |
|
|
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; 602.167; 600.097; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DLB2018 |
Serial |
3083 |
|
Permanent link to this record |
|
|
|
|
Author |
Anjan Dutta; Hichem Sahbi |
|
|
Title |
Stochastic Graphlet Embedding |
Type |
Journal Article |
|
Year |
2018 |
Publication |
IEEE Transactions on Neural Networks and Learning Systems |
Abbreviated Journal |
TNNLS |
|
|
Volume |
|
Issue |
|
Pages |
1-14 |
|
|
Keywords |
Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality |
|
|
Abstract |
Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of – explicit/implicit –graph vectorization and embedding. This embedding process
should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider
these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When
combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases. |
|
|
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; 602.167; 602.168; 600.097; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DuS2018 |
Serial |
3225 |
|
Permanent link to this record |
|
|
|
|
Author |
Mohamed Ali Souibgui; Y.Kessentini |
|
|
Title |
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement |
Type |
Journal Article |
|
Year |
2022 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
|
Volume |
44 |
Issue |
3 |
Pages |
1180-1191 |
|
|
Keywords |
|
|
|
Abstract |
Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems. |
|
|
Address |
1 March 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; 602.230; 600.121; 600.140 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SoK2022 |
Serial |
3454 |
|
Permanent link to this record |
|
|
|
|
Author |
David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados |
|
|
Title |
A Study of Bag-of-Visual-Words Representations for Handwritten Keyword Spotting |
Type |
Journal Article |
|
Year |
2015 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
|
|
Volume |
18 |
Issue |
3 |
Pages |
223-234 |
|
|
Keywords |
Bag-of-Visual-Words; Keyword spotting; Handwritten documents; Performance evaluation |
|
|
Abstract |
The Bag-of-Visual-Words (BoVW) framework has gained popularity among the document image analysis community, specifically as a representation of handwritten words for recognition or spotting purposes. Although in the computer vision field the BoVW method has been greatly improved, most of the approaches in the document image analysis domain still rely on the basic implementation of the BoVW method disregarding such latest refinements. In this paper, we present a review of those improvements and its application to the keyword spotting task. We thoroughly evaluate their impact against a baseline system in the well-known George Washington dataset and compare the obtained results against nine state-of-the-art keyword spotting methods. In addition, we also compare both the baseline and improved systems with the methods presented at the Handwritten Keyword Spotting Competition 2014. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer Berlin Heidelberg |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1433-2833 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; ADAS; 600.055; 600.061; 601.223; 600.077; 600.097 |
Approved |
no |
|
|
Call Number |
Admin @ si @ ART2015 |
Serial |
2679 |
|
Permanent link to this record |
|
|
|
|
Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Sergi Robles; Gemma Sanchez |
|
|
Title |
CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool |
Type |
Journal Article |
|
Year |
2015 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
|
|
Volume |
18 |
Issue |
1 |
Pages |
15-30 |
|
|
Keywords |
|
|
|
Abstract |
Recent results on structured learning methods have shown the impact of structural information in a wide range of pattern recognition tasks. In the field of document image analysis, there is a long experience on structural methods for the analysis and information extraction of multiple types of documents. Yet, the lack of conveniently annotated and free access databases has not benefited the progress in some areas such as technical drawing understanding. In this paper, we present a floor plan database, named CVC-FP, that is annotated for the architectural objects and their structural relations. To construct this database, we have implemented a groundtruthing tool, the SGT tool, that allows to make specific this sort of information in a natural manner. This tool has been made for general purpose groundtruthing: It allows to define own object classes and properties, multiple labeling options are possible, grants the cooperative work, and provides user and version control. We finally have collected some of the recent work on floor plan interpretation and present a quantitative benchmark for this database. Both CVC-FP database and the SGT tool are freely released to the research community to ease comparisons between methods and boost reproducible research. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer Berlin Heidelberg |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1433-2833 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; ADAS; 600.061; 600.076; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ HRR2015 |
Serial |
2567 |
|
Permanent link to this record |
|
|
|
|
Author |
Lluis Pere de las Heras; Ahmed Sheraz; Marcus Liwicki; Ernest Valveny; Gemma Sanchez |
|
|
Title |
Statistical Segmentation and Structural Recognition for Floor Plan Interpretation |
Type |
Journal Article |
|
Year |
2014 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
|
|
Volume |
17 |
Issue |
3 |
Pages |
221-237 |
|
|
Keywords |
|
|
|
Abstract |
A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer Berlin Heidelberg |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1433-2833 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; ADAS; 600.076; 600.077 |
Approved |
no |
|
|
Call Number |
HSL2014 |
Serial |
2370 |
|
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 |
Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate |
|
|
Title |
Feature Extraction by Using Dual-Generalized Discriminative Common Vectors |
Type |
Journal Article |
|
Year |
2019 |
Publication |
Journal of Mathematical Imaging and Vision |
Abbreviated Journal |
JMIV |
|
|
Volume |
61 |
Issue |
3 |
Pages |
331-351 |
|
|
Keywords |
Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning |
|
|
Abstract |
In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods. |
|
|
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; 600.084; 600.118; 600.121; 600.129 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DRR2019 |
Serial |
3172 |
|
Permanent link to this record |
|
|
|
|
Author |
Marçal Rusiñol; J. Chazalon; Katerine Diaz |
|
|
Title |
Augmented Songbook: an Augmented Reality Educational Application for Raising Music Awareness |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
|
|
Volume |
77 |
Issue |
11 |
Pages |
13773-13798 |
|
|
Keywords |
Augmented reality; Document image matching; Educational applications |
|
|
Abstract |
This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the idea of rhythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactic animations and interactive virtual content. Given a standard image processing pipeline, we compare the performance of different local descriptors at two key stages of the process. Results suggest alternatives to the SIFT local descriptors, regarding result quality and computational efficiency, both for document model identification and perspective transform estimation. All experiments are performed on an original and public dataset we introduce here. |
|
|
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; 600.084; 600.121; 600.118; 600.129 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RCD2018 |
Serial |
2996 |
|
Permanent link to this record |
|
|
|
|
Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri |
|
|
Title |
Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Journal of Mathematical Imaging and Vision |
Abbreviated Journal |
JMIV |
|
|
Volume |
60 |
Issue |
4 |
Pages |
512-524 |
|
|
Keywords |
|
|
|
Abstract |
This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies
have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model. |
|
|
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; 600.086; 600.130; 600.121; 600.118; 600.129 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DMH2018a |
Serial |
3062 |
|
Permanent link to this record |
|
|
|
|
Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Xavier Otazu; Horst Bunke |
|
|
Title |
A Combination of Features for Symbol-Independent Writer Identification in Old Music Scores |
Type |
Journal Article |
|
Year |
2010 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
|
|
Volume |
13 |
Issue |
4 |
Pages |
243-259 |
|
|
Keywords |
|
|
|
Abstract |
The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper, we present an architecture for writer identification in old handwritten music scores. Even though an important amount of music compositions contain handwritten text, the aim of our work is to use only music notation to determine the author. The main contribution is therefore the use of features extracted from graphical alphabets. Our proposal consists in combining the identification results of two different approaches, based on line and textural features. The steps of the ensemble architecture are the following. First of all, the music sheet is preprocessed for removing the staff lines. Then, music lines and texture images are generated for computing line features and textural features. Finally, the classification results are combined for identifying the writer. The proposed method has been tested on a database of old music scores from the seventeenth to nineteenth centuries, achieving a recognition rate of about 92% with 20 writers. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer-Verlag |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1433-2833 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; CAT;CIC |
Approved |
no |
|
|
Call Number |
FLS2010b |
Serial |
1319 |
|
Permanent link to this record |
|
|
|
|
Author |
Ernest Valveny; Enric Marti |
|
|
Title |
A model for image generation and symbol recognition through the deformation of lineal shapes |
Type |
Journal Article |
|
Year |
2003 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
|
Volume |
24 |
Issue |
15 |
Pages |
2857-2867 |
|
|
Keywords |
|
|
|
Abstract |
We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Elsevier Science Inc. |
Place of Publication |
New York, NY, USA |
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 |
DAG; IAM |
Approved |
no |
|
|
Call Number |
IAM @ iam @ VAM2003 |
Serial |
1653 |
|
Permanent link to this record |
|
|
|
|
Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
|
|
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 |
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 |
M. Visani; Oriol Ramos Terrades; Salvatore Tabbone |
|
|
Title |
A Protocol to Characterize the Descriptive Power and the Complementarity of Shape Descriptors |
Type |
Journal Article |
|
Year |
2011 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
|
|
Volume |
14 |
Issue |
1 |
Pages |
87-100 |
|
|
Keywords |
Document analysis; Shape descriptors; Symbol description; Performance characterization; Complementarity analysis |
|
|
Abstract |
Most document analysis applications rely on the extraction of shape descriptors, which may be grouped into different categories, each category having its own advantages and drawbacks (O.R. Terrades et al. in Proceedings of ICDAR’07, pp. 227–231, 2007). In order to improve the richness of their description, many authors choose to combine multiple descriptors. Yet, most of the authors who propose a new descriptor content themselves with comparing its performance to the performance of a set of single state-of-the-art descriptors in a specific applicative context (e.g. symbol recognition, symbol spotting...). This results in a proliferation of the shape descriptors proposed in the literature. In this article, we propose an innovative protocol, the originality of which is to be as independent of the final application as possible and which relies on new quantitative and qualitative measures. We introduce two types of measures: while the measures of the first type are intended to characterize the descriptive power (in terms of uniqueness, distinctiveness and robustness towards noise) of a descriptor, the second type of measures characterizes the complementarity between multiple descriptors. Characterizing upstream the complementarity of shape descriptors is an alternative to the usual approach where the descriptors to be combined are selected by trial and error, considering the performance characteristics of the overall system. To illustrate the contribution of this protocol, we performed experimental studies using a set of descriptors and a set of symbols which are widely used by the community namely ART and SC descriptors and the GREC 2003 database. |
|
|
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; IF 1.091 |
Approved |
no |
|
|
Call Number |
Admin @ si @VRT2011 |
Serial |
1856 |
|
Permanent link to this record |
|
|
|
|
Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Dimosthenis Karatzas |
|
|
Title |
Rotation Invariant Hand-Drawn Symbol Recognition based on a Dynamic Time Warping Model |
Type |
Journal Article |
|
Year |
2010 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
|
|
Volume |
13 |
Issue |
3 |
Pages |
229–241 |
|
|
Keywords |
|
|
|
Abstract |
One of the major difficulties of handwriting symbol recognition is the high variability among symbols because of the different writer styles. In this paper, we introduce a robust approach for describing and recognizing hand-drawn symbols tolerant to these writer style differences. This method, which is invariant to scale and rotation, is based on the dynamic time warping (DTW) algorithm. The symbols are described by vector sequences, a variation of the DTW distance is used for computing the matching distance, and K-Nearest Neighbor is used to classify them. Our approach has been evaluated in two benchmarking scenarios consisting of hand-drawn symbols. Compared with state-of-the-art methods for symbol recognition, our method shows higher tolerance to the irregular deformations induced by hand-drawn strokes. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer-Verlag |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1433-2833 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; IF 2009: 1,213 |
Approved |
no |
|
|
Call Number |
DAG @ dag @ FLS2010a |
Serial |
1288 |
|
Permanent link to this record |