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Author | Zhengying Liu; Zhen Xu; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Adrien Pavao; Sebastien Treguer; Wei-Wei Tu | ||||
Title | Towards automated computer vision: analysis of the AutoCV challenges 2019 | Type | Journal Article | ||
Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 135 | Issue | Pages | 196-203 | |
Keywords | Computer vision; AutoML; Deep learning | ||||
Abstract | We present the results of recent challenges in Automated Computer Vision (AutoCV, renamed here for clarity AutoCV1 and AutoCV2, 2019), which are part of a series of challenge on Automated Deep Learning (AutoDL). These two competitions aim at searching for fully automated solutions for classification tasks in computer vision, with an emphasis on any-time performance. The first competition was limited to image classification while the second one included both images and videos. Our design imposed to the participants to submit their code on a challenge platform for blind testing on five datasets, both for training and testing, without any human intervention whatsoever. Winning solutions adopted deep learning techniques based on already published architectures, such as AutoAugment, MobileNet and ResNet, to reach state-of-the-art performance in the time budget of the challenge (only 20 minutes of GPU time). The novel contributions include strategies to deliver good preliminary results at any time during the learning process, such that a method can be stopped early and still deliver good performance. This feature is key for the adoption of such techniques by data analysts desiring to obtain rapidly preliminary results on large datasets and to speed up the development process. The soundness of our design was verified in several aspects: (1) Little overfitting of the on-line leaderboard providing feedback on 5 development datasets was observed, compared to the final blind testing on the 5 (separate) final test datasets, suggesting that winning solutions might generalize to other computer vision classification tasks; (2) Error bars on the winners’ performance allow us to say with confident that they performed significantly better than the baseline solutions we provided; (3) The ranking of participants according to the any-time metric we designed, namely the Area under the Learning Curve, was different from that of the fixed-time metric, i.e. AUC at the end of the fixed time budget. We released all winning solutions under open-source licenses. At the end of the AutoDL challenge series, all data of the challenge will be made publicly available, thus providing a collection of uniformly formatted datasets, which can serve to conduct further research, particularly on meta-learning. | ||||
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Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ LXE2020 | Serial | 3427 | ||
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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 | ||
Call Number | BCNPCL @ bcnpcl @ OtP2006 | Serial | 658 | ||
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Author | Victor Ponce; Sergio Escalera; Marc Perez; Oriol Janes; Xavier Baro | ||||
Title | Non-Verbal Communication Analysis in Victim-Offender Mediations | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 67 | Issue | 1 | Pages | 19-27 |
Keywords | Victim–Offender Mediation; Multi-modal human behavior analysis; Face and gesture recognition; Social signal processing; Computer vision; Machine learning | ||||
Abstract | We present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. We propose the use of computer vision and social signal processing technologies in real scenarios of Victim–Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real Victim–Offender Mediation sessions in Catalonia. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state of the art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1–5] for the computed social signals. | ||||
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Notes | HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ PEP2015 | Serial | 2583 | ||
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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 | 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 | ||
Call Number | 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 | ||
Language | Summary Language | Original Title | |||
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Notes | MILAB;HUPBA | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ EPR2010e | Serial | 1338 | ||
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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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Abstract | 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 | ||
Area | Expedition | Conference | |||
Notes | MILAB;OR;HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ EMP2011 | Serial | 1714 | ||
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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 | ||
Call Number | BCNPCL @ bcnpcl @ EFP2009a | Serial | 1180 | ||
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Author | Pedro Martins; Paulo Carvalho; Carlo Gatta | ||||
Title | On the completeness of feature-driven maximally stable extremal regions | Type | Journal Article | ||
Year | 2016 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 74 | Issue | Pages | 9-16 | |
Keywords | Local features; Completeness; Maximally Stable Extremal Regions | ||||
Abstract | By definition, local image features provide a compact representation of the image in which most of the image information is preserved. This capability offered by local features has been overlooked, despite being relevant in many application scenarios. In this paper, we analyze and discuss the performance of feature-driven Maximally Stable Extremal Regions (MSER) in terms of the coverage of informative image parts (completeness). This type of features results from an MSER extraction on saliency maps in which features related to objects boundaries or even symmetry axes are highlighted. These maps are intended to be suitable domains for MSER detection, allowing this detector to provide a better coverage of informative image parts. Our experimental results, which were based on a large-scale evaluation, show that feature-driven MSER have relatively high completeness values and provide more complete sets than a traditional MSER detection even when sets of similar cardinality are considered. | ||||
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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 | LAMP;MILAB; | Approved | no | ||
Call Number | Admin @ si @ MCG2016 | Serial | 2748 | ||
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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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Abstract | 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 | Pau Riba; Josep Llados; Alicia Fornes | ||||
Title | Hierarchical graphs for coarse-to-fine error tolerant matching | Type | Journal Article | ||
Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 134 | Issue | Pages | 116-124 | |
Keywords | Hierarchical graph representation; Coarse-to-fine graph matching; Graph-based retrieval | ||||
Abstract | During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting). | ||||
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Notes | DAG; 600.097; 601.302; 603.057; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RLF2020 | Serial | 3349 | ||
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Author | Oriol Ramos Terrades; Ernest Valveny | ||||
Title | A new use of the ridgelets transform for describing linear singularities in images | Type | Journal Article | ||
Year | 2006 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 27 | Issue | 6 | Pages | 587–596 |
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Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ RaV2006a | Serial | 635 | ||
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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 | Miquel Ferrer; Ernest Valveny; F. Serratosa | ||||
Title | Median graph: A new exact algorithm using a distance based on the maximum common subgraph | Type | Journal Article | ||
Year | 2009 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 30 | Issue | 5 | Pages | 579–588 |
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Abstract | Median graphs have been presented as a useful tool for capturing the essential information of a set of graphs. Nevertheless, computation of optimal solutions is a very hard problem. In this work we present a new and more efficient optimal algorithm for the median graph computation. With the use of a particular cost function that permits the definition of the graph edit distance in terms of the maximum common subgraph, and a prediction function in the backtracking algorithm, we reduce the size of the search space, avoiding the evaluation of a great amount of states and still obtaining the exact median. We present a set of experiments comparing our new algorithm against the previous existing exact algorithm using synthetic data. In addition, we present the first application of the exact median graph computation to real data and we compare the results against an approximate algorithm based on genetic search. These experimental results show that our algorithm outperforms the previous existing exact algorithm and in addition show the potential applicability of the exact solutions to real problems. | ||||
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Publisher | Elsevier Science Inc. | Place of Publication | Editor | ||
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ISSN | 0167-8655 | ISBN | Medium | ||
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Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ FVS2009a | Serial | 1114 | ||
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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 | ||
Call Number | Admin @ si @ TEG2016 | Serial | 2843 | ||
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