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Author | Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta | ||||
Title | Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases | Type | Journal Article | ||
Year | 2017 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 87 | Issue | Pages | 203-211 | |
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Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations. However, retrieving a query graph from a large dataset of graphs implies a high computational complexity. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. With this aim, in this paper we propose a graph indexation formalism applied to visual retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Then, each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in different real scenarios such as handwritten word spotting in images of historical documents or symbol spotting in architectural floor plans. | ||||
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Notes | DAG; 600.097; 602.006; 603.053; 600.121 | Approved | no | ||
Call Number | RLF2017b | Serial | 2873 | ||
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Author | Thanh Nam Le; Muhammad Muzzamil Luqman; Anjan Dutta; Pierre Heroux; Christophe Rigaud; Clement Guerin; Pasquale Foggia; Jean Christophe Burie; Jean Marc Ogier; Josep Llados; Sebastien Adam | ||||
Title | Subgraph spotting in graph representations of comic book images | Type | Journal Article | ||
Year | 2018 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 112 | Issue | Pages | 118-124 | |
Keywords | Attributed graph; Region adjacency graph; Graph matching; Graph isomorphism; Subgraph isomorphism; Subgraph spotting; Graph indexing; Graph retrieval; Query by example; Dataset and comic book images | ||||
Abstract ![]() |
Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset. | ||||
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Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ LLD2018 | Serial | 3150 | ||
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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 | ||
Call Number | Admin @ si @ SFK2022 | Serial | 3736 | ||
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Author | Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Xavier Roca | ||||
Title | Efficient Discriminative Multiresolution Cascade for Real-Time Human Detection Applications | Type | Journal Article | ||
Year | 2011 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 32 | Issue | 13 | Pages | 1581-1587 |
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Human detection is fundamental in many machine vision applications, like video surveillance, driving assistance, action recognition and scene understanding. However in most of these applications real-time performance is necessary and this is not achieved yet by current detection methods.
This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a linear Support Vector Machine (SVM) composed of HOG features at different resolutions, from coarse at the first level to fine at the last one. In contrast to previous methods, our approach uses a non-uniform stride of the sliding window that is defined by the feature resolution and allows the detection to be incrementally refined as going from coarse-to-fine resolution. In this way, the speed-up of the cascade is not only due to the fewer number of features computed at the first levels of the cascade, but also to the reduced number of windows that need to be evaluated at the coarse resolution. Experimental results show that our method reaches a detection rate comparable with the state-of-the-art of detectors based on HOG features, while at the same time the detection search is up to 23 times faster. |
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ PGB2011a | Serial | 1707 | ||
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Author | Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol | ||||
Title | Minimal Design of Error-Correcting Output Codes | Type | Journal Article | ||
Year | 2011 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 33 | Issue | 6 | Pages | 693-702 |
Keywords | Multi-class classification; Error-correcting output codes; Ensemble of classifiers | ||||
Abstract ![]() |
IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers. |
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Publisher | Elsevier | Place of Publication | Editor | ||
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ISSN | 0167-8655 | ISBN | Medium | ||
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Notes | MILAB; OR;HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ BEB2011a | Serial | 1800 | ||
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Author | Sergio Escalera; David Masip; Eloi Puertas; Petia Radeva; Oriol Pujol | ||||
Title | Online Error-Correcting Output Codes | Type | Journal Article | ||
Year | 2011 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 32 | Issue | 3 | Pages | 458-467 |
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IF JCR CCIA 1.303 2009 54/103
This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier. |
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Publisher | Elsevier | Place of Publication | North Holland | Editor | |
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ISSN | 0167-8655 | ISBN | Medium | ||
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Notes | MILAB;OR;HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ EMP2011 | Serial | 1714 | ||
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Author | A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva | ||||
Title | Topological principal component analysis for face encoding and recognition | Type | Journal Article | ||
Year | 2001 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 22 | Issue | 6-7 | Pages | 769–776 |
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Abstract ![]() |
IF: 0.552 | ||||
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Notes | ADAS;OR;MV | Approved | no | ||
Call Number | ADAS @ adas @ PVL2001 | Serial | 155 | ||
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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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IF: 0.809 | ||||
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Notes | OR;MV | Approved | no | ||
Call Number | 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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IF: 0.809 | ||||
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Notes | MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ CaR2003 | Serial | 371 | ||
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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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IF: 0.809 | ||||
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Notes | OR;MV | Approved | no | ||
Call Number | 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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IF: 0.809 | ||||
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Notes | OR;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ GVS2003 | Serial | 382 | ||
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Author | A. Sanfeliu; Juan J. Villanueva | ||||
Title | An approach of visual motion analysis | Type | Journal Article | ||
Year | 2005 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 26 | Issue | 3 | Pages | 355–368 |
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IF: 1.138 | ||||
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Notes | Approved | no | |||
Call Number | ISE @ ise @ SaV2005 | Serial | 561 | ||
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Author | Jaume Amores; Petia Radeva | ||||
Title | Registration and Retrieval of Highly Elastic Bodies using Contextual Information | Type | Journal Article | ||
Year | 2005 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 26 | Issue | 11 | Pages | 1720–1731 |
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IF: 1.138 | ||||
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Notes | ADAS;MILAB | Approved | no | ||
Call Number | ADAS @ adas @ AmR2005b | Serial | 592 | ||
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Author | Arka Ujjal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit | ||||
Title | Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding | Type | Journal Article | ||
Year | 2021 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 149 | Issue | Pages | 164-171 | |
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Abstract ![]() |
Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art. | ||||
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Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ DGV2021 | Serial | 3364 | ||
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Author | Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados | ||||
Title | A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages | Type | Journal Article | ||
Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 136 | Issue | Pages | 219-227 | |
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Abstract ![]() |
In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks. | ||||
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Notes | DAG; 600.140; 601.311; 600.121 | Approved | no | ||
Call Number | Admin @ si @ CFV2020 | Serial | 3451 | ||
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