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	Author	Bogdan Raducanu; Fadi Dornaika
	Title	A Discriminative Non-Linear Manifold Learning Technique for Face Recognition			Type	Book Chapter
	Year	2011	Publication	Informatics Engineering and Information Science	Abbreviated Journal
	Volume	254	Issue	6	Pages	339-353
	Keywords
	Abstract	In this paper we propose a novel non-linear discriminative analysis technique for manifold learning. The proposed approach is a discriminant version of Laplacian Eigenmaps which takes into account the class label information in order to guide the procedure of non-linear dimensionality reduction. By following the large margin concept, the graph Laplacian is split in two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques. The experimental results confirm that our method outperforms, in general, the existing ones. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variance in their appearance.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1865-0929	ISBN	978-3-642-25482-6	Medium
	Area		Expedition		Conference	ICIEIS
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ RaD2011			Serial	1804
Permanent link to this record



	Author	Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
	Title	Current Challenges on Polyp Detection in Colonoscopy Videos: From Region Segmentation to Region Classification. a Pattern Recognition-based Approach.ased Approach			Type	Conference Article
	Year	2011	Publication	2nd International Workshop on Medical Image Analysis and Descriptionfor Diagnosis Systems	Abbreviated Journal
	Volume		Issue		Pages	62-71
	Keywords	Medical Imaging, Colonoscopy, Pattern Recognition, Segmentation, Polyp Detection, Region Description, Machine Learning, Real-time.
	Abstract	In this paper we present our approach on real-time polyp detection in colonoscopy videos. Our method consists of three stages: Image Segmentation, Region Description and Image Classification. Taking into account the constraints of our project, we introduce our segmentation system that is based on the model of appearance of the polyp that we have defined after observing real videos from colonoscopy processes. The output of this stage will ideally be a low number of regions of which one of them should cover the whole polyp region (if there is one in the image). This regions will be described in terms of features and, as a result of a machine learning schema, classified based on the values that they have for the several features that we will use on their description. Although we are still on the early stages of the project, we present some preliminary segmentation results that indicates that we are going in a good direction.
	Address	Rome, Italy
	Corporate Author				Thesis
	Publisher	SciTePress	Place of Publication		Editor	Djemal, Khalifa
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area	800	Expedition		Conference	MIAD
	Notes	MV;SIAI			Approved	no
	Call Number	IAM @ iam @ BSV2011a			Serial	1695
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	Author	Francesco Ciompi; Oriol Pujol; Carlo Gatta; Xavier Carrillo; J. Mauri; Petia Radeva
	Title	A Holistic Approach for the Detection of Media-Adventitia Border in IVUS			Type	Conference Article
	Year	2011	Publication	14th International Conference on Medical Image Computing and Computer Assisted Intervention	Abbreviated Journal
	Volume	6893	Issue		Pages	401-408
	Keywords
	Abstract	In this paper we present a methodology for the automatic detection of media-adventitia border (MAb) in Intravascular Ultrasound. A robust computation of the MAb is achieved through a holistic approach where the position of the MAb with respect to other tissues of the vessel is used. A learned quality measure assures that the resulting MAb is optimal with respect to all other tissues. The mean distance error computed through a set of 140 images is 0.2164 (±0.1326) mm.
	Address	Toronto, Canada
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-23625-9	Medium
	Area		Expedition		Conference	MICCAI
	Notes	MILAB;HuPBA			Approved	no
	Call Number	Admin @ si @ CPG2011			Serial	1739
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	Author	Daniel Ponsa; Joan Serrat; Antonio Lopez
	Title	On-board image-based vehicle detection and tracking			Type	Journal Article
	Year	2011	Publication	Transactions of the Institute of Measurement and Control	Abbreviated Journal	TIM
	Volume	33	Issue	7	Pages	783-805
	Keywords	vehicle detection
	Abstract	In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time.
	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	ADAS			Approved	no
	Call Number	ADAS @ adas @ PSL2011			Serial	1413
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	Author	Jorge Bernal; Fernando Vilariño; F. Javier Sanchez
	Title	Towards Intelligent Systems for Colonoscopy			Type	Book Chapter
	Year	2011	Publication	Colonoscopy	Abbreviated Journal
	Volume	1	Issue		Pages	257-282
	Keywords
	Abstract	In this chapter we present tools that can be used to build intelligent systems for colonoscopy. The idea is, by using methods based on computer vision and artiﬁcial intelligence, add signiﬁcant value to the colonoscopy procedure. Intelligent systems are being used to assist in other medical interventions
	Address
	Corporate Author				Thesis
	Publisher	Intech	Place of Publication		Editor	Paul Miskovitz
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-953-307-568-6	Medium
	Area	800	Expedition		Conference
	Notes	MV;SIAI			Approved	no
	Call Number	IAM @ iam @ BVS2011			Serial	1697
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	Author	Alicia Fornes; Anjan Dutta; Albert Gordo; Josep Llados
	Title	The ICDAR 2011 Music Scores Competition: Staff Removal and Writer Identification			Type	Conference Article
	Year	2011	Publication	11th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	1511-1515
	Keywords
	Abstract	In the last years, there has been a growing interest in the analysis of handwritten music scores. In this sense, our goal has been to foster the interest in the analysis of handwritten music scores by the proposal of two different competitions: Staff removal and Writer Identification. Both competitions have been tested on the CVC-MUSCIMA database: a ground-truth of handwritten music score images. This paper describes the competition details, including the dataset and ground-truth, the evaluation metrics, and a short description of the participants, their methods, and the obtained results.
	Address	Beijing, China
	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	978-0-7695-4520-2	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ FDG2011b			Serial	1794
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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
	Keywords
	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.
	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			Approved	no
	Call Number	Admin @ si @ SDP2011			Serial	1727
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	Author	Santiago Segui
	Title	Contributions to the Diagnosis of Intestinal Motility by Automatic Image Analysis			Type	Book Whole
	Year	2011	Publication	PhD Thesis, Universitat de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In the early twenty first century Given Imaging Ltd. presented wireless capsule endoscopy (WCE) as a new technological breakthrough that allowed the visualization of the intestine by using a small, swallowed camera. This small size device was received with a high enthusiasm within the medical community, and until now, it is still one of the medical devices with the highest use growth rate. WCE can be used as a novel diagnostic tool that presents several clinical advantages, since it is non-invasive and at the same time it provides, for the first time, a full picture of the small bowel morphology, contents and dynamics. Since its appearance, the WCE has been used to detect several intestinal dysfunctions such as: polyps, ulcers and bleeding. However, the visual analysis of WCE videos presents an important drawback: the long time required by the physicians for proper video visualization. In this sense and regarding to this limitation, the development of computer aided systems is required for the extensive use of WCE in the medical community. The work presented in this thesis is a set of contributions for the automatic image analysis and computer-aided diagnosis of intestinal motility disorders using WCE. Until now, the diagnosis of small bowel motility dysfunctions was basically performed by invasive techniques such as the manometry test, which can only be conducted at some referral centers around the world owing to the complexity of the procedure and the medial expertise required in the interpretation of the results. Our contributions are divided in three main blocks: 1. Image analysis by computer vision techniques to detect events in the endoluminal WCE scene. Several methods have been proposed to detect visual events such as: intestinal contractions, intestinal content, tunnel and wrinkles; 2. Machine learning techniques for the analysis and the manipulation of the data from WCE. These methods have been proposed in order to overcome the problems that the analysis of WCE presents such as: video acquisition cost, unlabeled data and large number of data; 3. Two different systems for the computer-aided diagnosis of intestinal motility disorders using WCE. The first system presents a fully automatic method that aids at discriminating healthy subjects from patients with severe intestinal motor disorders like pseudo-obstruction or food intolerance. The second system presents another automatic method that models healthy subjects and discriminate them from mild intestinal motility patients.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Jordi Vitria
	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			Approved	no
	Call Number	Admin @ si @ Seg2011			Serial	1836
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	Author	David Roche; Debora Gil; Jesus Giraldo
	Title	An inference model for analyzing termination conditions of Evolutionary Algorithms			Type	Conference Article
	Year	2011	Publication	14th Congrès Català en Intel·ligencia Artificial	Abbreviated Journal
	Volume		Issue		Pages	216-225
	Keywords	Evolutionary Computation Convergence, Termination Conditions, Statistical Inference
	Abstract	In real-world problems, it is mandatory to design a termination condition for Evolutionary Algorithms (EAs) ensuring stabilization close to the unknown optimum. Distribution-based quantities are good candidates as far as suitable parameters are used. A main limitation for application to real-world problems is that such parameters strongly depend on the topology of the objective function, as well as, the EA paradigm used. We claim that the termination problem would be fully solved if we had a model measuring to what extent a distribution-based quantity asymptotically behaves like the solution accuracy. We present a regression-prediction model that relates any two given quantities and reports if they can be statistically swapped as termination conditions. Our framework is applied to two issues. First, exploring if the parameters involved in the computation of distribution-based quantities influence their asymptotic behavior. Second, to what extent existing distribution-based quantities can be asymptotically exchanged for the accuracy of the EA solution.
	Address	Lleida, Catalonia (Spain)
	Corporate Author	Associació Catalana Intel·ligència Artificial			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	978-1-60750-841-0	Medium
	Area		Expedition		Conference	CCIA
	Notes	IAM			Approved	no
	Call Number	IAM @ iam @ RGG2011a			Serial	1677
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	Author	Albert Gordo; Florent Perronnin
	Title	Asymmetric Distances for Binary Embeddings			Type	Conference Article
	Year	2011	Publication	IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	729 - 736
	Keywords
	Abstract	In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique – PCA Embedding (PCAE) – which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH.
	Address	Providence, RI
	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	978-1-4577-0394-2	Medium
	Area		Expedition		Conference	CVPR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ GoP2011; IAM @ iam @ GoP2011			Serial	1817
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	Author	Eduard Vazquez
	Title	Unsupervised image segmentation based on material reflectance description and saliency			Type	Book Whole
	Year	2011	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Image segmentations aims to partition an image into a set of non-overlapped regions, called segments. Despite the simplicity of the definition, image segmentation raises as a very complex problem in all its stages. The definition of segment is still unclear. When asking to a human to perform a segmentation, this person segments at different levels of abstraction. Some segments might be a single, well-defined texture whereas some others correspond with an object in the scene which might including multiple textures and colors. For this reason, segmentation is divided in bottom-up segmentation and top-down segmentation. Bottom up-segmentation is problem independent, that is, focused on general properties of the images such as textures or illumination. Top-down segmentation is a problem-dependent approach which looks for specific entities in the scene, such as known objects. This work is focused on bottom-up segmentation. Beginning from the analysis of the lacks of current methods, we propose an approach called RAD. Our approach overcomes the main shortcomings of those methods which use the physics of the light to perform the segmentation. RAD is a topological approach which describes a single-material reflectance. Afterwards, we cope with one of the main problems in image segmentation: non supervised adaptability to image content. To yield a non-supervised method, we use a model of saliency yet presented in this thesis. It computes the saliency of the chromatic transitions of an image by means of a statistical analysis of the images derivatives. This method of saliency is used to build our final approach of segmentation: spRAD. This method is a non-supervised segmentation approach. Our saliency approach has been validated with a psychophysical experiment as well as computationally, overcoming a state-of-the-art saliency method. spRAD also outperforms state-of-the-art segmentation techniques as results obtained with a widely-used segmentation dataset show
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher		Place of Publication		Editor	Ramon Baldrich
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ Vaz2011b			Serial	1835
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	Author	David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin
	Title	Cool world: domain adaptation of virtual and real worlds for human detection using active learning			Type	Conference Article
	Year	2011	Publication	NIPS Domain Adaptation Workshop: Theory and Application	Abbreviated Journal	NIPS-DA
	Volume		Issue		Pages
	Keywords	Pedestrian Detection; Virtual; Domain Adaptation; Active Learning
	Abstract	Image based human detection is of paramount interest for different applications. The most promising human detectors rely on discriminatively learnt classifiers, i.e., trained with labelled samples. However, labelling is a manual intensive task, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, in Marin et al. we have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera and the same type of scenario. Accordingly, in Vazquez et al. we cast the problem as one of supervised domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we use an active learning technique. Thus, ultimately our human model is learnt by the combination of virtual- and real-world labelled samples which, to the best of our knowledge, was not done before. Here, we term such combined space cool world. In this extended abstract we summarize our proposal, and include quantitative results from Vazquez et al. showing its validity.
	Address	Granada, Spain
	Corporate Author				Thesis
	Publisher		Place of Publication	Granada, Spain	Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	DA-NIPS
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ VLP2011b			Serial	1756
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	Author	David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin
	Title	Virtual Worlds and Active Learning for Human Detection			Type	Conference Article
	Year	2011	Publication	13th International Conference on Multimodal Interaction	Abbreviated Journal
	Volume		Issue		Pages	393-400
	Keywords	Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning
	Abstract	Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid.
	Address	Alicante, Spain
	Corporate Author				Thesis
	Publisher	ACM DL	Place of Publication	New York, NY, USA, USA	Editor
	Language	English	Summary Language	English	Original Title	Virtual Worlds and Active Learning for Human Detection
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4503-0641-6	Medium
	Area		Expedition		Conference	ICMI
	Notes	ADAS			Approved	yes
	Call Number	ADAS @ adas @ VLP2011a			Serial	1683
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	Author	Maria Salamo; Sergio Escalera
	Title	Increasing Retrieval Quality in Conversational Recommenders			Type	Journal Article
	Year	2011	Publication	IEEE Transactions on Knowledge and Data Engineering	Abbreviated Journal	TKDE
	Volume	99	Issue		Pages	1-1
	Keywords
	Abstract	IF JCR CCIA 2.286 2009 24/103 JCR Impact Factor 2010: 1.851 A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing speciﬁc preference values. Incremental Critiquing is a conversational recommender system that uses critiquing as a feedback to efﬁciently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user’s soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns Turing the recommendation process, we propose two novel exponential reinforcement learning approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisﬁed critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a global weighting approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology signiﬁcantly improves recommendation efﬁciency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches
	Address
	Corporate Author				Thesis
	Publisher	IEEE	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1041-4347	ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB; HuPBA			Approved	no
	Call Number	Admin @ si @ SaE2011			Serial	1713
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	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
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