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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 |
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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 | Naveen Onkarappa; Angel Sappa | ||||
| Title | A Novel Space Variant Image Representation | Type | Journal Article | ||
| Year | 2013 | Publication | Journal of Mathematical Imaging and Vision | Abbreviated Journal | JMIV |
| Volume |
47 | Issue | 1-2 | Pages | 48-59 |
| Keywords | Space-variant representation; Log-polar mapping; Onboard vision applications | ||||
| Abstract | Traditionally, in machine vision images are represented using cartesian coordinates with uniform sampling along the axes. On the contrary, biological vision systems represent images using polar coordinates with non-uniform sampling. For various advantages provided by space-variant representations many researchers are interested in space-variant computer vision. In this direction the current work proposes a novel and simple space variant representation of images. The proposed representation is compared with the classical log-polar mapping. The log-polar representation is motivated by biological vision having the characteristic of higher resolution at the fovea and reduced resolution at the periphery. On the contrary to the log-polar, the proposed new representation has higher resolution at the periphery and lower resolution at the fovea. Our proposal is proved to be a better representation in navigational scenarios such as driver assistance systems and robotics. The experimental results involve analysis of optical flow fields computed on both proposed and log-polar representations. Additionally, an egomotion estimation application is also shown as an illustrative example. The experimental analysis comprises results from synthetic as well as real sequences. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0924-9907 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.055; 605.203; 601.215 | Approved | no | ||
| Call Number | Admin @ si @ OnS2013a | Serial | 2243 | ||
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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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| Author | Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny | ||||
| Title | Segmentation-free Word Spotting with Exemplar SVMs | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
47 | Issue | 12 | Pages | 3967–3978 |
| Keywords | Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression | ||||
| Abstract | In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding-window approach is used to locate the document regions that are most similar to the query. We use the Exemplar SVM framework to produce a better representation of the query in an unsupervised way. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage. | ||||
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| Notes | DAG; 600.045; 600.056; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ AGF2014b | Serial | 2485 | ||
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| Author | Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo | ||||
| Title | Leveraging local neighborhood topology for large scale person re-identification | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
47 | Issue | 12 | Pages | 3767–3778 |
| Keywords | Re-identification; Conditional random field; Semi-supervised; ETHZ; CAVIAR; 3DPeS; CMV100 | ||||
| Abstract | In this paper we describe a semi-supervised approach to person re-identification that combines discriminative models of person identity with a Conditional Random Field (CRF) to exploit the local manifold approximation induced by the nearest neighbor graph in feature space. The linear discriminative models learned on few gallery images provides coarse separation of probe images into identities, while a graph topology defined by distances between all person images in feature space leverages local support for label propagation in the CRF. We evaluate our approach using multiple scenarios on several publicly available datasets, where the number of identities varies from 28 to 191 and the number of images ranges between 1003 and 36 171. We demonstrate that the discriminative model and the CRF are complementary and that the combination of both leads to significant improvement over state-of-the-art approaches. We further demonstrate how the performance of our approach improves with increasing test data and also with increasing amounts of additional unlabeled data. | ||||
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| Notes | LAMP; 601.240; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ KLB2014a | Serial | 2522 | ||
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| Author | Marc Bolaños; Mariella Dimiccoli; Petia Radeva | ||||
| Title | Towards Storytelling from Visual Lifelogging: An Overview | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Human-Machine Systems | Abbreviated Journal | THMS |
| Volume |
47 | Issue | 1 | Pages | 77 - 90 |
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| Abstract | Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging. |
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| Notes | MILAB; 601.235 | Approved | no | ||
| Call Number | Admin @ si @ BDR2017 | Serial | 2712 | ||
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| Author | Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores | ||||
| Title | On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on cybernetics | Abbreviated Journal | Cyber |
| Volume |
47 | Issue | 11 | Pages | 3980 - 3990 |
| Keywords | Multicue; multimodal; multiview; object detection | ||||
| Abstract | Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. | ||||
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| ISSN | 2168-2267 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.085; 600.082; 600.076; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 2810 | ||
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| Author | F. Moreso; D. Seron; Jordi Vitria; J.M. Grinyo; F.M. Colome-Serra; N. Pares; J.R. Serra | ||||
| Title | Quantification of Interstitial Chronic Renal Damage by means of Texture Analysis. | Type | Journal Article | ||
| Year | 1994 | Publication | Kidney International | Abbreviated Journal | |
| Volume |
46 | Issue | 6 | Pages | 1721-1727 |
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ MSV1994 | Serial | 113 | ||
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| Author | Albert Gordo; Florent Perronnin; Ernest Valveny | ||||
| Title | Large-scale document image retrieval and classification with runlength histograms and binary embeddings | Type | Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
46 | Issue | 7 | Pages | 1898-1905 |
| Keywords | visual document descriptor; compression; large-scale; retrieval; classification | ||||
| Abstract | We present a new document image descriptor based on multi-scale runlength
histograms. This descriptor does not rely on layout analysis and can be computed efficiently. We show how this descriptor can achieve state-of-theart results on two very different public datasets in classification and retrieval tasks. Moreover, we show how we can compress and binarize these descriptors to make them suitable for large-scale applications. We can achieve state-ofthe- art results in classification using binary descriptors of as few as 16 to 64 bits. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | DAG; 600.042; 600.045; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ GPV2013 | Serial | 2306 | ||
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| Author | Albert Gordo; Alicia Fornes; Ernest Valveny | ||||
| Title | Writer identification in handwritten musical scores with bags of notes | Type | Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
46 | Issue | 5 | Pages | 1337-1345 |
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| Abstract | Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GFV2013 | Serial | 2307 | ||
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| Author | Veronica Romero; Alicia Fornes; Nicolas Serrano; Joan Andreu Sanchez; A.H. Toselli; Volkmar Frinken; E. Vidal; Josep Llados | ||||
| Title | The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition | Type | Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
46 | Issue | 6 | Pages | 1658-1669 |
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| Abstract | Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demography studies and genealogical research. Automatic processing of historical documents, however, has mostly been focused on single works of literature and less on social records, which tend to have a distinct layout, structure, and vocabulary. Such information is usually collected by expert demographers that devote a lot of time to manually transcribe them. This paper presents a new database, compiled from a marriage license books collection, to support research in automatic handwriting recognition for historical documents containing social records. Marriage license books are documents that were used for centuries by ecclesiastical institutions to register marriage licenses. Books from this collection are handwritten and span nearly half a millennium until the beginning of the 20th century. In addition, a study is presented about the capability of state-of-the-art handwritten text recognition systems, when applied to the presented database. Baseline results are reported for reference in future studies. | ||||
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| Publisher | Elsevier Science Inc. New York, NY, USA | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | DAG; 600.045; 602.006; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ RFS2013 | Serial | 2298 | ||
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| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
| Title | A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding | Type | Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
46 | Issue | 10 | Pages | 2830-2839 |
| Keywords | Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification | ||||
| Abstract | Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGE2013a | Serial | 2247 | ||
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| Author | Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard | ||||
| Title | Fuzzy Multilevel Graph Embedding | Type | Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
46 | Issue | 2 | Pages | 551-565 |
| Keywords | Pattern recognition; Graphics recognition; Graph clustering; Graph classification; Explicit graph embedding; Fuzzy logic | ||||
| Abstract | Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | DAG; 600.042; 600.045; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ LRL2013a | Serial | 2270 | ||
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| Author | Anjan Dutta; Josep Llados; Umapada Pal | ||||
| Title | A symbol spotting approach in graphical documents by hashing serialized graphs | Type | Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
46 | Issue | 3 | Pages | 752-768 |
| Keywords | Symbol spotting; Graphics recognition; Graph matching; Graph serialization; Graph factorization; Graph paths; Hashing | ||||
| Abstract | In this paper we propose a symbol spotting technique in graphical documents. Graphs are used to represent the documents and a (sub)graph matching technique is used to detect the symbols in them. We propose a graph serialization to reduce the usual computational complexity of graph matching. Serialization of graphs is performed by computing acyclic graph paths between each pair of connected nodes. Graph paths are one-dimensional structures of graphs which are less expensive in terms of computation. At the same time they enable robust localization even in the presence of noise and distortion. Indexing in large graph databases involves a computational burden as well. We propose a graph factorization approach to tackle this problem. Factorization is intended to create a unified indexed structure over the database of graphical documents. Once graph paths are extracted, the entire database of graphical documents is indexed in hash tables by locality sensitive hashing (LSH) of shape descriptors of the paths. The hashing data structure aims to execute an approximate k-NN search in a sub-linear time. We have performed detailed experiments with various datasets of line drawings and compared our method with the state-of-the-art works. The results demonstrate the effectiveness and efficiency of our technique. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | DAG; 600.042; 600.045; 605.203; 601.152 | Approved | no | ||
| Call Number | Admin @ si @ DLP2012 | Serial | 2127 | ||
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