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Author |
Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
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Title |
Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes |
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Journal Article |
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Year |
2009 |
Publication |
Journal of Signal Processing Systems |
Abbreviated Journal |
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Volume |
55 |
Issue |
1-3 |
Pages |
35–47 |
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Abstract |
Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches. |
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1939-8018 |
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Notes |
MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ EPM2009 |
Serial |
1258 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Traffic sign recognition system with β -correction |
Type |
Journal Article |
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Year |
2010 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVA |
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Volume |
21 |
Issue |
2 |
Pages |
99–111 |
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Abstract |
Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success. |
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Springer-Verlag |
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0932-8092 |
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MILAB;HUPBA |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2010a |
Serial |
1276 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
On the Decoding Process in Ternary Error-Correcting Output Codes |
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Journal Article |
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Year |
2010 |
Publication |
IEEE on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
32 |
Issue |
1 |
Pages |
120–134 |
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Abstract |
A common way to model multiclass classification problems is to design a set of binary classifiers and to combine them. Error-correcting output codes (ECOC) represent a successful framework to deal with these type of problems. Recent works in the ECOC framework showed significant performance improvements by means of new problem-dependent designs based on the ternary ECOC framework. The ternary framework contains a larger set of binary problems because of the use of a ldquodo not carerdquo symbol that allows us to ignore some classes by a given classifier. However, there are no proper studies that analyze the effect of the new symbol at the decoding step. In this paper, we present a taxonomy that embeds all binary and ternary ECOC decoding strategies into four groups. We show that the zero symbol introduces two kinds of biases that require redefinition of the decoding design. A new type of decoding measure is proposed, and two novel decoding strategies are defined. We evaluate the state-of-the-art coding and decoding strategies over a set of UCI machine learning repository data sets and into a real traffic sign categorization problem. The experimental results show that, following the new decoding strategies, the performance of the ECOC design is significantly improved. |
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0162-8828 |
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Notes |
MILAB;HUPBA |
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BCNPCL @ bcnpcl @ EPR2010b |
Serial |
1277 |
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Author |
Fernando Vilariño; Panagiota Spyridonos; Fosca De Iorio; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
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Title |
Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions |
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Journal Article |
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Year |
2010 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
TMI |
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Volume |
29 |
Issue |
2 |
Pages |
246-259 |
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Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions shown in a video provided by an ingestible capsule with a wireless micro-camera. The manual labeling of all the motility events requires large amount of time for offline screening in search of findings with low prevalence, which turns this procedure currently unpractical. In this paper, we propose a machine learning system to automatically detect the phasic intestinal contractions in video capsule endoscopy, driving a useful but not feasible clinical routine into a feasible clinical procedure. Our proposal is based on a sequential design which involves the analysis of textural, color, and blob features together with SVM classifiers. Our approach tackles the reduction of the imbalance rate of data and allows the inclusion of domain knowledge as new stages in the cascade. We present a detailed analysis, both in a quantitative and a qualitative way, by providing several measures of performance and the assessment study of interobserver variability. Our system performs at 70% of sensitivity for individual detection, whilst obtaining equivalent patterns to those of the experts for density of contractions. |
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IEEE |
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Edition |
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ISSN |
0278-0062 |
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800 |
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Notes |
MILAB;MV;OR;SIAI |
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no |
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Call Number |
BCNPCL @ bcnpcl @ VSD2010; IAM @ iam @ VSI2010 |
Serial |
1281 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria; Maria Teresa Anguera |
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Title |
Automatic Detection of Dominance and Expected Interest |
Type |
Journal Article |
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Year |
2010 |
Publication |
EURASIP Journal on Advances in Signal Processing |
Abbreviated Journal |
EURASIPJ |
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12 |
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Article ID 491819
Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems. |
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ISSN |
1110-8657 |
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Notes |
OR;MILAB;HUPBA;MV |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2010d |
Serial |
1283 |
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