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Author | Oriol Pujol; Sergio Escalera; Petia Radeva | ||||
Title | An Incremental Node Embedding Technique for Error Correcting Output Codes | Type | Journal | ||
Year | 2008 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 41 | Issue | 2 | Pages | 713–725 |
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Notes | MILAB;HuPBA | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ PER2008 | Serial | 942 | ||
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Author | Miquel Ferrer; Ernest Valveny; F. Serratosa | ||||
Title | Median Graphs: A Genetic Approach based on New Theoretical Properties | Type | Journal Article | ||
Year | 2009 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 42 | Issue | 9 | Pages | 2003–2012 |
Keywords | Median graph; Genetic search; Maximum common subgraph; Graph matching; Structural pattern recognition | ||||
Abstract | Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present two major contributions. On one side, and from a theoretical point of view, we show new theoretical properties of the median graph. On the other side, using these new properties, we present a new approximate algorithm based on the genetic search, that improves the computation of the median graph. Finally, we perform a set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity. With these results, we show how the concept of the median graph can be used in real applications and leaves the box of the only-theoretical concepts, demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs. | ||||
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Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ FVS2009b | Serial | 1167 | ||
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Author | Daniel Ponsa; Antonio Lopez | ||||
Title | Variance reduction techniques in particle-based visual contour Tracking | Type | Journal Article | ||
Year | 2009 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 42 | Issue | 11 | Pages | 2372–2391 |
Keywords | Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling | ||||
Abstract | This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done. | ||||
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ PoL2009a | Serial | 1168 | ||
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Author | Miquel Ferrer; Ernest Valveny; F. Serratosa; K. Riesen; Horst Bunke | ||||
Title | Generalized Median Graph Computation by Means of Graph Embedding in Vector Spaces | Type | Journal Article | ||
Year | 2010 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 43 | Issue | 4 | Pages | 1642–1655 |
Keywords | Graph matching; Weighted mean of graphs; Median graph; Graph embedding; Vector spaces | ||||
Abstract | The median graph has been presented as a useful tool to represent a set of graphs. Nevertheless its computation is very complex and the existing algorithms are restricted to use limited amount of data. In this paper we propose a new approach for the computation of the median graph based on graph embedding. Graphs are embedded into a vector space and the median is computed in the vector domain. We have designed a procedure based on the weighted mean of a pair of graphs to go from the vector domain back to the graph domain in order to obtain a final approximation of the median graph. Experiments on three different databases containing large graphs show that we succeed to compute good approximations of the median graph. We have also applied the median graph to perform some basic classification tasks achieving reasonable good results. These experiments on real data open the door to the application of the median graph to a number of more complex machine learning algorithms where a representative of a set of graphs is needed. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ FVS2010 | Serial | 1294 | ||
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Author | Joan Mas; Josep Llados; Gemma Sanchez; J.A. Jorge | ||||
Title | A syntactic approach based on distortion-tolerant Adjacency Grammars and a spatial-directed parser to interpret sketched diagrams | Type | Journal Article | ||
Year | 2010 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 43 | Issue | 12 | Pages | 4148–4164 |
Keywords | Syntactic Pattern Recognition; Symbol recognition; Diagram understanding; Sketched diagrams; Adjacency Grammars; Incremental parsing; Spatial directed parsing | ||||
Abstract | This paper presents a syntactic approach based on Adjacency Grammars (AG) for sketch diagram modeling and understanding. Diagrams are a combination of graphical symbols arranged according to a set of spatial rules defined by a visual language. AG describe visual shapes by productions defined in terms of terminal and non-terminal symbols (graphical primitives and subshapes), and a set functions describing the spatial arrangements between symbols. Our approach to sketch diagram understanding provides three main contributions. First, since AG are linear grammars, there is a need to define shapes and relations inherently bidimensional using a sequential formalism. Second, our parsing approach uses an indexing structure based on a spatial tessellation. This serves to reduce the search space when finding candidates to produce a valid reduction. This allows order-free parsing of 2D visual sentences while keeping combinatorial explosion in check. Third, working with sketches requires a distortion model to cope with the natural variations of hand drawn strokes. To this end we extended the basic grammar with a distortion measure modeled on the allowable variation on spatial constraints associated with grammar productions. Finally, the paper reports on an experimental framework an interactive system for sketch analysis. User tests performed on two real scenarios show that our approach is usable in interactive settings. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ MLS2010 | Serial | 1336 | ||
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Author | Umapada Pal; Partha Pratim Roy; N. Tripathya; Josep Llados | ||||
Title | Multi-oriented Bangla and Devnagari text recognition | Type | Journal Article | ||
Year | 2010 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 43 | Issue | 12 | Pages | 4124–4136 |
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Abstract | There are printed complex documents where text lines of a single page may have different orientations or the text lines may be curved in shape. As a result, it is difficult to detect the skew of such documents and hence character segmentation and recognition of such documents are a complex task. In this paper, using background and foreground information we propose a novel scheme towards the recognition of Indian complex documents of Bangla and Devnagari script. In Bangla and Devnagari documents usually characters in a word touch and they form cavity regions. To take care of these cavity regions, background information of such documents is used. Convex hull and water reservoir principle have been applied for this purpose. Here, at first, the characters are segmented from the documents using the background information of the text. Next, individual characters are recognized using rotation invariant features obtained from the foreground part of the characters.
For character segmentation, at first, writing mode of a touching component (word) is detected using water reservoir principle based features. Next, depending on writing mode and the reservoir base-region of the touching component, a set of candidate envelope points is then selected from the contour points of the component. Based on these candidate points, the touching component is finally segmented into individual characters. For recognition of multi-sized/multi-oriented characters the features are computed from different angular information obtained from the external and internal contour pixels of the characters. These angular information are computed in such a way that they do not depend on the size and rotation of the characters. Circular and convex hull rings have been used to divide a character into smaller zones to get zone-wise features for higher recognition results. We combine circular and convex hull features to improve the results and these features are fed to support vector machines (SVM) for recognition. From our experiment we obtained recognition results of 99.18% (98.86%) accuracy when tested on 7515 (7874) Devnagari (Bangla) characters. |
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ PRT2010 | Serial | 1337 | ||
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Author | Palaiahnakote Shivakumara; Anjan Dutta; Trung Quy Phan; Chew Lim Tan; Umapada Pal | ||||
Title | A Novel Mutual Nearest Neighbor based Symmetry for Text Frame Classification in Video | Type | Journal Article | ||
Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 44 | Issue | 8 | Pages | 1671-1683 |
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Abstract | In the field of multimedia retrieval in video, text frame classification is essential for text detection, event detection, event boundary detection, etc. We propose a new text frame classification method that introduces a combination of wavelet and median moment with k-means clustering to select probable text blocks among 16 equally sized blocks of a video frame. The same feature combination is used with a new Max–Min clustering at the pixel level to choose probable dominant text pixels in the selected probable text blocks. For the probable text pixels, a so-called mutual nearest neighbor based symmetry is explored with a four-quadrant formation centered at the centroid of the probable dominant text pixels to know whether a block is a true text block or not. If a frame produces at least one true text block then it is considered as a text frame otherwise it is a non-text frame. Experimental results on different text and non-text datasets including two public datasets and our own created data show that the proposed method gives promising results in terms of recall and precision at the block and frame levels. Further, we also show how existing text detection methods tend to misclassify non-text frames as text frames in term of recall and precision at both the block and frame levels. | ||||
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Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ SDP2011 | Serial | 1727 | ||
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Author | Carlo Gatta; Eloi Puertas; Oriol Pujol | ||||
Title | Multi-Scale Stacked Sequential Learning | Type | Journal Article | ||
Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 44 | Issue | 10-11 | Pages | 2414-2416 |
Keywords | Stacked sequential learning; Multiscale; Multiresolution; Contextual classification | ||||
Abstract | One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Notes | MILAB;HuPBA | Approved | no | ||
Call Number | Admin @ si @ GPP2011 | Serial | 1802 | ||
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Author | Partha Pratim Roy; Umapada Pal; Josep Llados | ||||
Title | Document Seal Detection Using Ght and Character Proximity Graphs | Type | Journal Article | ||
Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 44 | Issue | 6 | Pages | 1282-1295 |
Keywords | Seal recognition; Graphical symbol spotting; Generalized Hough transform; Multi-oriented character recognition | ||||
Abstract | This paper deals with automatic detection of seal (stamp) from documents with cluttered background. Seal detection involves a difficult challenge due to its multi-oriented nature, arbitrary shape, overlapping of its part with signature, noise, etc. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors computed from recognition result of individual connected components (characters). Scale and rotation invariant features are used in a Support Vector Machine (SVM) classifier to recognize multi-scale and multi-oriented text characters. The concept of generalized Hough transform (GHT) is used to detect the seal and a voting scheme is designed for finding possible location of the seal in a document based on the spatial feature descriptor of neighboring component pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal in a document. Experiment is performed in an archive of historical documents of handwritten/printed English text. Experimental results show that the method is robust in locating seal instances of arbitrary shape and orientation in documents, and also efficient in indexing a collection of documents for retrieval purposes. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ RPL2011 | Serial | 1820 | ||
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Author | Laura Igual; Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Fernando De la Torre | ||||
Title | Continuous Generalized Procrustes Analysis | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 2 | Pages | 659–671 |
Keywords | Procrustes analysis; 2D shape model; Continuous approach | ||||
Abstract | PR4883, PII: S0031-3203(13)00327-0
Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, the standard GPA process suffers from three main limitations. Firstly, the 2D training samples do not necessarily cover a uniform sampling of all the 3D transformations of an object. This can bias the estimate of the shape model. Secondly, it can be computationally expensive to learn the shape model by sampling 3D transformations. Thirdly, standard GPA methods use only one reference shape, which can might be insufficient to capture large structural variability of some objects. To address these drawbacks, this paper proposes continuous generalized Procrustes analysis (CGPA). CGPA uses a continuous formulation that avoids the need to generate 2D projections from all the rigid 3D transformations. It builds an efficient (in space and time) non-biased 2D shape model from a set of 3D model of objects. A major challenge in CGPA is the need to integrate over the space of 3D rotations, especially when the rotations are parameterized with Euler angles. To address this problem, we introduce the use of the Haar measure. Finally, we extended CGPA to incorporate several reference shapes. Experimental results on synthetic and real experiments show the benefits of CGPA over GPA. |
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Notes | OR; HuPBA; 605.203; 600.046;MILAB | Approved | no | ||
Call Number | Admin @ si @ IPE2014 | Serial | 2352 | ||
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Author | Bogdan Raducanu; Fadi Dornaika | ||||
Title | Embedding new observations via sparse-coding for non-linear manifold learning | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 1 | Pages | 480-492 |
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Abstract | Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data-the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that the sparse representation theory not only serves for automatic graph construction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the K-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on six public face datasets. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes. | ||||
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Notes | LAMP; | Approved | no | ||
Call Number | Admin @ si @ RaD2013b | Serial | 2316 | ||
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Author | Volkmar Frinken; Andreas Fischer; Markus Baumgartner; Horst Bunke | ||||
Title | Keyword spotting for self-training of BLSTM NN based handwriting recognition systems | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 3 | Pages | 1073-1082 |
Keywords | Document retrieval; Keyword spotting; Handwriting recognition; Neural networks; Semi-supervised learning | ||||
Abstract | The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. | ||||
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Notes | DAG; 600.077; 602.101 | Approved | no | ||
Call Number | Admin @ si @ FFB2014 | Serial | 2297 | ||
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Author | Miguel Angel Bautista; Sergio Escalera; Oriol Pujol | ||||
Title | On the Design of an ECOC-Compliant Genetic Algorithm | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 2 | Pages | 865-884 |
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Abstract | Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches. | ||||
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Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ BEP2013 | Serial | 2254 | ||
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Author | Pierluigi Casale; Oriol Pujol; Petia Radeva | ||||
Title | Approximate polytope ensemble for one-class classification | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 2 | Pages | 854-864 |
Keywords | One-class classification; Convex hull; High-dimensionality; Random projections; Ensemble learning | ||||
Abstract | In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets. | ||||
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Notes | MILAB; 605.203 | Approved | no | ||
Call Number | Admin @ si @ CPR2014a | Serial | 2469 | ||
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Author | Francesco Ciompi; Oriol Pujol; Petia Radeva | ||||
Title | ECOC-DRF: Discriminative random fields based on error correcting output codes | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 6 | Pages | 2193-2204 |
Keywords | Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models | ||||
Abstract | We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments. | ||||
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Notes | LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 | Approved | no | ||
Call Number | Admin @ si @ CPR2014b | Serial | 2470 | ||
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