| Home | << 1 2 3 4 >> |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov | ||||
| Title | Fast: Facilitated and accurate scene text proposals through fcn guided pruning | Type | Journal Article | ||
| Year | 2019 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 119 | Issue | Pages | 112-120 | |
| Keywords | |||||
| Abstract | Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition. | ||||
| 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; 600.084; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ BGN2019 | Serial | 3342 | ||
| Permanent link to this record | |||||
| 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). | ||||
| 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; 600.097; 601.302; 603.057; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RLF2020 | Serial | 3349 | ||
| Permanent link to this record | |||||
| 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 | |
| Keywords | |||||
| 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. | ||||
| 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 | Approved | no | ||
| Call Number | Admin @ si @ DGV2021 | Serial | 3364 | ||
| Permanent link to this record | |||||
| 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. | ||||
| 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 | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ LXE2020 | Serial | 3427 | ||
| Permanent link to this record | |||||
| 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 | |
| Keywords | |||||
| 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. | ||||
| 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; 601.311; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CFV2020 | Serial | 3451 | ||
| Permanent link to this record | |||||
| Author | B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols | ||||
| Title | Knowledge graph based methods for record linkage | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 136 | Issue | Pages | 127-133 | |
| Keywords | |||||
| Abstract | Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.
In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results. |
||||
| 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 @ GRP2020 | Serial | 3453 | ||
| Permanent link to this record | |||||
| Author | Eduardo Aguilar; Petia Radeva | ||||
| Title | Uncertainty-aware integration of local and flat classifiers for food recognition | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 136 | Issue | Pages | 237-243 | |
| Keywords | |||||
| Abstract | Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen. | ||||
| 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 | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ AgR2020 | Serial | 3525 | ||
| Permanent link to this record | |||||
| Author | Carola Figueroa Flores; David Berga; Joost Van de Weijer; Bogdan Raducanu | ||||
| Title | Saliency for free: Saliency prediction as a side-effect of object recognition | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 150 | Issue | Pages | 1-7 | |
| Keywords | Saliency maps; Unsupervised learning; Object recognition | ||||
| Abstract | Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects instead of the background. So far, computational methods for saliency estimation required the explicit generation of a saliency map, process which is usually achieved via eyetracking experiments on still images. This is a tedious process that needs to be repeated for each new dataset. In the current paper, we demonstrate that is possible to automatically generate saliency maps without ground-truth. In our approach, saliency maps are learned as a side effect of object recognition. Extensive experiments carried out on both real and synthetic datasets demonstrated that our approach is able to generate accurate saliency maps, achieving competitive results when compared with supervised 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 | LAMP; 600.147; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ FBW2021 | Serial | 3559 | ||
| Permanent link to this record | |||||
| 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. | ||||
| 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 | LAMP; 600.147; 601.379; 600.120; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ WWH2021 | Serial | 3575 | ||
| Permanent link to this record | |||||
| Author | Lluis Gomez; Ali Furkan Biten; Ruben Tito; Andres Mafla; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | Multimodal grid features and cell pointers for scene text visual question answering | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 150 | Issue | Pages | 242-249 | |
| Keywords | |||||
| Abstract | This paper presents a new model for the task of scene text visual question answering. In this task questions about a given image can only be answered by reading and understanding scene text. Current state of the art models for this task make use of a dual attention mechanism in which one attention module attends to visual features while the other attends to textual features. A possible issue with this is that it makes difficult for the model to reason jointly about both modalities. To fix this problem we propose a new model that is based on an single attention mechanism that attends to multi-modal features conditioned to the question. The output weights of this attention module over a grid of multi-modal spatial features are interpreted as the probability that a certain spatial location of the image contains the answer text to the given question. Our experiments demonstrate competitive performance in two standard datasets with a model that is faster than previous methods at inference time. Furthermore, we also provide a novel analysis of the ST-VQA dataset based on a human performance study. Supplementary material, code, and data is made available through this link. | ||||
| 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; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GBT2021 | Serial | 3620 | ||
| Permanent link to this record | |||||
| Author | Marçal Rusiñol; Agnes Borras; Josep Llados | ||||
| Title | Relational Indexing of Vectorial Primitives for Symbol Spotting in Line-Drawing Images | Type | Journal Article | ||
| Year | 2010 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 31 | Issue | 3 | Pages | 188–201 |
| Keywords | Document image analysis and recognition, Graphics recognition, Symbol spotting ,Vectorial representations, Line-drawings | ||||
| Abstract | This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an off-the-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher |
Elsevier | 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 | Approved | no | ||
| Call Number | DAG @ dag @ RBL2010 | Serial | 1177 | ||
| Permanent link to this record | |||||
| Author | Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados | ||||
| Title | Unsupervised writer adaptation of whole-word HMMs with application to word-spotting | Type | Journal Article | ||
| Year | 2010 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 31 | Issue | 8 | Pages | 742–749 |
| Keywords | Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis | ||||
| Abstract | In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters.
Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition. |
||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher |
Elsevier | 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 | Approved | no | ||
| Call Number | DAG @ dag @ RPS2010 | Serial | 1290 | ||
| Permanent link to this record | |||||
| 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 |
| Keywords | |||||
| 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. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher |
Elsevier | 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 | MILAB;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EPR2010e | Serial | 1338 | ||
| Permanent link to this record | |||||
| Author | Josep Llados; Horst Bunke; Enric Marti | ||||
| Title | Finding rotational symmetries by cyclic string matching | Type | Journal Article | ||
| Year | 1997 | Publication | Pattern recognition letters | Abbreviated Journal | PRL |
| Volume | 18 | Issue | 14 | Pages | 1435-1442 |
| Keywords | Rotational symmetry; Reflectional symmetry; String matching | ||||
| Abstract | Symmetry is an important shape feature. In this paper, a simple and fast method to detect perfect and distorted rotational symmetries of 2D objects is described. The boundary of a shape is polygonally approximated and represented as a string. Rotational symmetries are found by cyclic string matching between two identical copies of the shape string. The set of minimum cost edit sequences that transform the shape string to a cyclically shifted version of itself define the rotational symmetry and its order. Finally, a modification of the algorithm is proposed to detect reflectional symmetries. Some experimental results are presented to show the reliability of the proposed algorithm | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher |
Elsevier | 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; | Approved | no | ||
| Call Number | IAM @ iam @ LBM1997a | Serial | 1562 | ||
| Permanent link to this record | |||||
| 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 |
| Keywords | |||||
| 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. |
||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher |
Elsevier | Place of Publication | North Holland | 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 | MILAB;OR;HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ EMP2011 | Serial | 1714 | ||
| Permanent link to this record | |||||