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Jaume Amores; N. Sebe; Petia Radeva |
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Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier |
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2006 |
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Pattern Recognition Letters |
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27 |
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3 |
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201–209 |
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ADAS;MILAB |
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ADAS @ adas @ ASR2006 |
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643 |
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Author |
Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
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Title |
ROC curves and video analysis optimization in intestinal capsule endoscopy |
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Journal Article |
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Year |
2006 |
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Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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27 |
Issue |
8 |
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875–881 |
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ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
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Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
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800 |
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MILAB;MV;SIAI |
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BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
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647 |
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Xavier Otazu; Oriol Pujol |
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Wavelet based approach to cluster analysis. Application on low dimensional data sets |
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2006 |
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Pattern Recognition Letters |
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PRL |
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27 |
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14 |
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1590–1605 |
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MILAB; CIC; HuPBA |
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BCNPCL @ bcnpcl @ OtP2006 |
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658 |
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Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes |
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2009 |
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Pattern Recognition Letters |
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30 |
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3 |
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285–297 |
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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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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPR2009a |
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1153 |
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Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados |
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Title |
Blurred Shape Model for Binary and Grey-level Symbol Recognition |
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2009 |
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Pattern Recognition Letters |
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30 |
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15 |
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1424–1433 |
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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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HuPBA; DAG; MILAB |
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BCNPCL @ bcnpcl @ EFP2009a |
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1180 |
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