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Carl Frélicot - IEEE Xplore Author Profile
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Carl Frélicot

Also published under: C. Frelicot, Carl Frelicot, C. Frélicot

Publications
26
Citations
347
Publications by Year
19922016
Co-Authors:
Show All Co-Authors (17)
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Carl Frélicot

Also published under: C. Frelicot, Carl Frelicot, C. Frélicot

Affiliation

Images et Applications
Université de La Rochelle
La Rochelle, France

Publication Topics

Biography

Carl Frélicot received the Ph.D. degree in system control in 1992 from the Compiegne University of Technology, Compiegne, France.,He joined the University of La Rochelle, La Rochelle, France, as an Assistant Professor in 1993, where he has been the Head of the Pattern Recognition Group of the L3i laboratory for two years. He is currently a Full Professor and the Head of the Computer Science Department, University of La Rochelle. His research interest include algebraic operators applied to information fusion, statistical and fuzzy pattern recognition, and image and video analysis.(Based on document published on 22 January 2007).
Publications
26
Citations
347
Publications by Year
19922016
Co-Authors:
Show All Co-Authors (17)
Author's Published Works

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Online Weighted One-Class Ensemble for feature selection in background/foreground separation

Caroline Silva;Thierry Bouwmans;Carl Frélicot

2016 23rd International Conference on Pattern Recognition (ICPR)
Year: 2016 | Conference Paper |
Cited by: Papers (10)
Background subtraction (BS) is one of the key steps for detecting moving objects in video surveillance applications. In the last few years, many BS methods have been developed to handle the different challenges met in video surveillance but the role and the relevance of the visual features used has been less investigated. In this paper, we present an Online Weighted Ensemble of One-Class SVMs (Sup...Show More

Online Weighted One-Class Ensemble for feature selection in background/foreground separation

Caroline Silva;Thierry Bouwmans;Carl Frélicot

Year: 2016 | Conference Paper |

Binary codes K-modes clustering for HSI segmentation

Michel Berthier;Sandallah El Asmar;Carl Frélicot

2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)
Year: 2016 | Conference Paper |
Cited by: Papers (1)
An new pixel unsupervised hyperspectral image (HSI) segmentation method is proposed. It relies on a binary incoding of spectral reflectance curve variations of pixels that allows to consider HSI segmentation as a clustering problem in the feature set of binary strings. Using a generalized Hamming distance, a k-modes algorithm is applied to obtain a cluster partionning of the HSI with no use of any...Show More

Binary codes K-modes clustering for HSI segmentation

Michel Berthier;Sandallah El Asmar;Carl Frélicot

Year: 2016 | Conference Paper |

A normalized soft window-based similarity measure to extend the Rand index

Romain Quéré;Carl Frélicot

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)
Year: 2011 | Conference Paper |
This article addresses the problem of the construction of concordance measures between two crisp, fuzzy and possibilistic partitions from their coincidence matrices. Two existing approaches are reviewed and their advantages and drawbacks are exhibited so that a new Rand index taking profits of both is proposed. Numerous experimental results show that it outperforms other extensions of the Rand ind...Show More

A normalized soft window-based similarity measure to extend the Rand index

Romain Quéré;Carl Frélicot

Year: 2011 | Conference Paper |

A Cluster-Validity Index Combining an Overlap Measure and a Separation Measure Based on Fuzzy-Aggregation Operators

Hoel Le Capitaine;Carl Frelicot

IEEE Transactions on Fuzzy Systems
Year: 2011 | Volume: 19, Issue: 3 | Journal Article |
Cited by: Papers (48)
Since a clustering algorithm can produce as many partitions as desired, one needs to assess their quality in order to select the partition that most represents the structure in the data, if there is any. This is the rationale for the cluster-validity (CV) problem and indices. This paper presents a CV index that helps to find the optimal number of clusters of data from partitions generated by a fuz...Show More

A Cluster-Validity Index Combining an Overlap Measure and a Separation Measure Based on Fuzzy-Aggregation Operators

Hoel Le Capitaine;Carl Frelicot

Year: 2011 | Volume: 19, Issue: 3 | Journal Article |

On Normalizing Fuzzy Coincidence Matrices to Compare Fuzzy and/or Possibilistic Partitions with the Rand Index

R. Quéré;H. Le Capitaine;N. Fraisseix;C. Frélicot

2010 IEEE International Conference on Data Mining
Year: 2010 | Conference Paper |
Cited by: Papers (6)
Most already existing indices used to compare two strict partitions with different number of clusters are based on coincidence matrices. To extend such indices to fuzzy partitions, one can define fuzzy coincidence matrices by means of triangular norms. It has been shown this can require some kind of normalization to reinforce the corresponding indices. We propose in this paper a generic solution t...Show More

On Normalizing Fuzzy Coincidence Matrices to Compare Fuzzy and/or Possibilistic Partitions with the Rand Index

R. Quéré;H. Le Capitaine;N. Fraisseix;C. Frélicot

Year: 2010 | Conference Paper |

An Optimum Class-Rejective Decision Rule and Its Evaluation

Hoel Le Capitaine;Carl Frélicot

2010 20th International Conference on Pattern Recognition
Year: 2010 | Conference Paper |
Cited by: Papers (7)
Decision-making systems intend to copy human reasoning which often consists in eliminating highly non probable situations (e.g. diseases, suspects) rather than selecting the most reliable ones. In this paper, we present the concept of class-rejective rules for pattern recognition. Contrary to usual reject option schemes where classes are selected when they may correspond to the true class of the i...Show More

An Optimum Class-Rejective Decision Rule and Its Evaluation

Hoel Le Capitaine;Carl Frélicot

Year: 2010 | Conference Paper |

On Selecting an Optimal Number of Clusters for Color Image Segmentation

Hoel Le Capitaine;Carl Frélicot

2010 20th International Conference on Pattern Recognition
Year: 2010 | Conference Paper |
Cited by: Papers (2)
This paper addresses the problem of region-based color image segmentation using a fuzzy clustering algorithm, e.g. a spatial version of fuzzy c-means, in order to partition the image into clusters corresponding to homogeneous regions. We propose to determine the optimal number of clusters, and so the number of regions, by using a new cluster validity index computed on fuzzy partitions. Experimenta...Show More

On Selecting an Optimal Number of Clusters for Color Image Segmentation

Hoel Le Capitaine;Carl Frélicot

Year: 2010 | Conference Paper |

On (weighted) k-order fuzzy connectives

Hoel Le Capitaine;Carl Frélicot

International Conference on Fuzzy Systems
Year: 2010 | Conference Paper |
Cited by: Papers (1)
In this paper, we present new fuzzy connectives that allow to specify an order to the considered operation. These operators are generalization of usual fuzzy connectives, i.e. triangular norms and triangular conorms. A potential use of the proposed operators consists in assessing to what extent several values are high or low in unconstrained fuzzy sets is given. We also present weighted k-order fu...Show More

On (weighted) k-order fuzzy connectives

Hoel Le Capitaine;Carl Frélicot

Year: 2010 | Conference Paper |

A fuzzy modeling approach to cluster validity

Hoel Le Capitaine;Carl Frelicot

2009 IEEE International Conference on Fuzzy Systems
Year: 2009 | Conference Paper |
Cited by: Papers (3)
This paper presents a new approach to find the optimal number of clusters of a fuzzy partition. It is based on a fuzzy modeling approach which combines measures of clusters' separation and overlap. Theses measures are based on triangular norms and a discrete Sugeno integral. Results on artificial and real data sets prove its efficiency compared to indexes from the literature.Show More

A fuzzy modeling approach to cluster validity

Hoel Le Capitaine;Carl Frelicot

Year: 2009 | Conference Paper |

A class-selective rejection scheme based on blockwise similarity of typicality degrees

Hoel Le Capitaine;Carl Frelicot

2008 19th International Conference on Pattern Recognition
Year: 2008 | Conference Paper |
Cited by: Papers (2)
Overlapping classes and outliers can significantly decrease a classifier performance. We address here the problem of giving a classifier the ability to reject some patterns either for ambiguity or for distance in order to improve its performance. Given a set of typicality degrees for a pattern to be classified, we use an operator based on triangular norms and a discrete Sugeno integral to quantify...Show More

A class-selective rejection scheme based on blockwise similarity of typicality degrees

Hoel Le Capitaine;Carl Frelicot

Year: 2008 | Conference Paper |

Blockwise similarity in [0,1] via triangular norms and Sugeno integrals - Application to cluster validity

Hoel Le Capitaine;Thomas Batard;Carl Frelicot;Michel Berthier

2007 IEEE International Fuzzy Systems Conference
Year: 2007 | Conference Paper |
Cited by: Papers (3)
In many fields, e.g. decision-making, numerical values in [0,1] are available and one is often interested in detecting which are similar. In this paper, we propose an operator which is able to detect whether some values can be gathered by blocks with respect to their similarity or not. It combines the values and a kernel function using triangular norms and Sugeno integrals. This operator allows to...Show More

Blockwise similarity in [0,1] via triangular norms and Sugeno integrals - Application to cluster validity

Hoel Le Capitaine;Thomas Batard;Carl Frelicot;Michel Berthier

Year: 2007 | Conference Paper |

Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search

Alexis Joly;Olivier Buisson;Carl Frelicot

IEEE Transactions on Multimedia
Year: 2007 | Volume: 9, Issue: 2 | Journal Article |
Content-based copy retrieval (CBCR) aims at retrieving in a database all the modified versions or the previous versions of a given candidate object. In this paper, we present a copy-retrieval scheme based on local features that can deal with very large databases both in terms of quality and speed. We first propose a new approximate similarity search technique in which the probabilistic selection o...Show More

Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search

Alexis Joly;Olivier Buisson;Carl Frelicot

Year: 2007 | Volume: 9, Issue: 2 | Journal Article |

A New Cluster Validity Index for Fuzzy Clustering based on Combination of Dual Triples

C. Frelicot;L. Mascarilla;M. Besthier

2006 IEEE International Conference on Fuzzy Systems
Year: 2006 | Conference Paper |
Cited by: Papers (5)
Cluster validity indexes aim at evaluating the degree to which a partition obtained from a clustering algorithm approximates the real structure of a data set. Most of them reduce to the search of the right number of clusters. This paper presents such a new validity index for fuzzy clustering based on the aggregation of the resulting membership degrees with no additional information, e.g. lite geom...Show More

A New Cluster Validity Index for Fuzzy Clustering based on Combination of Dual Triples

C. Frelicot;L. Mascarilla;M. Besthier

Year: 2006 | Conference Paper |

Statistical similarity search applied to content-based video copy detection

A. Joly;O. Buisson;C. Frelicot

21st International Conference on Data Engineering Workshops (ICDEW'05)
Year: 2005 | Conference Paper |
Content-based copy detection (CBCD) is one of the emerging multimedia applications for which there is a need of a concerted effort from the database community and the computer vision community. Recent methods based on interest points and local fingerprints have been proposed to perform robust CBCD of images and video. They include two steps: the search of similar fingerprints in the database and a...Show More

Statistical similarity search applied to content-based video copy detection

A. Joly;O. Buisson;C. Frelicot

Year: 2005 | Conference Paper |

Content-based video copy detection in large databases: a local fingerprints statistical similarity search approach

A. Joly;C. Frelicot;O. Buisson

IEEE International Conference on Image Processing 2005
Year: 2005 | Conference Paper |
Recent methods based on interest points and local fingerprints have been proposed to perform robust CBCD (content-based copy detection) of images and video. They include two steps: the search for similar local fingerprints in the database (DB) and a voting strategy that merges all the local results in order to perform a global decision. In most image or video retrieval systems, the search for simi...Show More

Content-based video copy detection in large databases: a local fingerprints statistical similarity search approach

A. Joly;C. Frelicot;O. Buisson

Year: 2005 | Volume: 1 | Conference Paper |

Feature statistical retrieval applied to content based copy identification

A. Joly;C. Frelicot;O. Buisson

2004 International Conference on Image Processing, 2004. ICIP '04.
Year: 2004 | Conference Paper |
Cited by: Papers (15)
In many image or video retrieval systems, the search for similar objects in the database includes a spatial access method to a multidimensional feature space. This step is generally considered as a problem independent of the features and the similarity type. The well known multidimensional nearest neighbor search has also been widely studied by the database community as a generic method. We propos...Show More

Feature statistical retrieval applied to content based copy identification

A. Joly;C. Frelicot;O. Buisson

Year: 2004 | Volume: 1 | Conference Paper |

School level recognition from children's drawings and writing

C. Frelicot;C. Remi;P. Courtellemont

2002 International Conference on Pattern Recognition
Year: 2002 | Conference Paper |
Cited by: Papers (2)
This paper presents part of the work aiming at building a tool for the detection of graphomotor difficulties involving disorders in the writing of children. We have defined an experimental protocol, containing exercises such as copying figures or writing sentences under different conditions. It allows to measure simple aspects of graphomotor skill up to complex ones. A great number of features wer...Show More

School level recognition from children's drawings and writing

C. Frelicot;C. Remi;P. Courtellemont

Year: 2002 | Volume: 3 | Conference Paper |

A third way to design pattern classifiers with reject options

C. Frelicot;L. Mascarilla

2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)
Year: 2002 | Conference Paper |
Cited by: Papers (3)
The design of a rejection-based classifier can be made according to two well-identified strategies operating in two sequential steps: the accept-first strategy and the reject-first one. The first one is the most usual. Recently, we have proposed a general class of the latter classifiers using fuzzy XOR operators based on dual triples (t-norm, t-conorm, complement) (2001). In this paper, we investi...Show More

A third way to design pattern classifiers with reject options

C. Frelicot;L. Mascarilla

Year: 2002 | Conference Paper |

A class of reject-first possibilistic classifiers

L. Mascarilla;C. Frelicot

Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)
Year: 2001 | Conference Paper |
Cited by: Papers (1)
The authors address the problem of defining a general class of reject-first possibilistic classifiers. It relies on fuzzy XOR operators based on dual triples (t-norm, t-conorm, complement). Such a classifier operates in two sequential steps. It starts with testing for exclusive classification by thresholding the fuzzy XOR combination of membership degrees to the different classes. If the pattern h...Show More

A class of reject-first possibilistic classifiers

L. Mascarilla;C. Frelicot

Year: 2001 | Volume: 2 | Conference Paper |

Another look at combining rejection-based pattern classifiers

L. Mascarilla;C. Frelicot

Proceedings 15th International Conference on Pattern Recognition. ICPR-2000
Year: 2000 | Conference Paper |
In the context of pattern classification with two-fold reject options (ambiguity and distance), we have identified different strategies leading to two-stage classifiers. Strategies differ on managing both types of rejection (independently or not). The paper addresses the problem of combining the first stage of rejection-based classifiers of each strategy. A Dempster-Shafer model is designed. Its m...Show More

Another look at combining rejection-based pattern classifiers

L. Mascarilla;C. Frelicot

Year: 2000 | Volume: 2 | Conference Paper |

Combining rejection-based pattern classifiers

L. Mascarilla;C. Frelicot

PeachFuzz 2000. 19th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.00TH8500)
Year: 2000 | Conference Paper |
Cited by: Papers (2)
This article deals with the combination of the first stage of two-fold rejection-based classifiers for pattern classification. This Dempster-Shafer's model-based combination uses some relevant characteristics of the different two-stage classifier strategies we have identified. These strategies differ on the managing of the ambiguity and distance rejection (independently or not). We propose some cl...Show More

Combining rejection-based pattern classifiers

L. Mascarilla;C. Frelicot

Year: 2000 | Conference Paper |

Combining: an alternative for choosing between reject-first and accept-first classifiers

L. Mascarilla;C. Frelicot;E. Zahzah

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)
Year: 1999 | Conference Paper |
Cited by: Papers (2)
In the general framework of pattern classification two reject strategies are identified. These classifiers are defined as a couple of labeling and hardening functions. In this paper the first stage of classifiers belonging to each strategy are used in turn and their results are combined with a Dempster-Shafer model to classify or reject patterns. Results on artificial data are provided.Show More

Combining: an alternative for choosing between reject-first and accept-first classifiers

L. Mascarilla;C. Frelicot;E. Zahzah

Year: 1999 | Conference Paper |

A pretopology-based supervised pattern classifier

C. Frelicot;F. Lebourgeois

Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170)
Year: 1998 | Conference Paper |
Cited by: Papers (2)
Deals with the pretopological approach to the supervised pattern classification problem. An improved learning process which results in a reduced number of neighborhoods to be learned is proposed. We also present extensions concerning the use of different metrics in the /spl isin/-neighborhood definition in order to improve the class boundaries. Performances in terms of storage requirements, comput...Show More

A pretopology-based supervised pattern classifier

C. Frelicot;F. Lebourgeois

Year: 1998 | Volume: 1 | Conference Paper |

A rejection-based possibilistic classifier and its parameters learning

C. Frelicot

1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228)
Year: 1998 | Conference Paper |
Cited by: Papers (5)
This paper presents a rejection-based and class-selective possibilistic classifier and its parameters learning. The classifier is defined as a couple of functions (D,T), D being a labelling one and T being a hardening one in a non-exclusive way. The parameters of the classifier (D,T), whose strategy for rejection is not classical, are learned using a suitable clustering algorithm and statistical o...Show More

A rejection-based possibilistic classifier and its parameters learning

C. Frelicot

Year: 1998 | Volume: 2 | Conference Paper |

Multiprototype-based fuzzy classification and reject options

C. Frelicot

Proceedings of IEEE 5th International Fuzzy Systems
Year: 1996 | Conference Paper |
Cited by: Papers (2)
This paper aims at presenting different classifiers (classification rules) with two characteristics. First, they are based on multiprototype fuzzy labels which are combined using connectives (t-conorms). Thus, the definition of the classes increases and consequently the classifier performance. Second, the rules include reject options. They allow the classifiers to manage uncertainty due to both im...Show More

Multiprototype-based fuzzy classification and reject options

C. Frelicot

Year: 1996 | Volume: 3 | Conference Paper |

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