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! Logo retrieval with a contrario visual query expansion [[http://www-rocq.inria.fr/~ajoly/pmh-revised.pdf | [ACM09] ]]



NEW!! Go to BelgaLogos home page


! Interactive objects retrieval with efficient boosting [[http://www-rocq.inria.fr/~ajoly/pmh-revised.pdf | [ACM09] ]]


! Multi-probe locality sensitive hashing [[http://www-rocq.inria.fr/~ajoly/pmh-revised.pdf | [ACM08] ]]
We developed, jointly with Olivier Buisson at INA, a new similarity search structure dedicated to high dimensional features. Multi-probe LSH is built on the well-known LSH technique, but it intelligently probes multiple buckets that are likely to contain query results in a hash table. Our method is inspired by our previous work on probabilistic similarity search structures and improves upon
recent theoretical work on multi-probe and query adaptive
LSH. Whereas these methods are based on likelihood criteria
that a given bucket contains query results, we define a more
reliable a posteriori model taking account some prior about
the queries and the searched objects. This prior knowledge
allows a better quality control of the search and a more
accurate selection of the most probable buckets. We implemented a nearest neighbors search based on this paradigm
and performed experiments on different real visual features
datasets. We show that our a posteriori scheme outperforms
other multi-probe LSH while offering a better quality control. Comparisons to the basic LSH technique show that our
method allows consistent improvements both in space and
time efficiency.


! Content-based Video Copy Detection
The last research paper somehow summarizing my work on this topic: [[http://www-rocq.inria.fr/~ajoly/ajolyTMA07.pdf| [TMA07] ]]

My [+[[Main.MyPhdResearchActivitiesAtINA|My Phd at INA]]+] is hopefully also still of interest ;-)

I am currently less involved on research issues about CBCD but still strongly implied in benchmarking (more info [[http://www-rocq.inria.fr/~ajoly/index.php?n=Main.ContentBasedCopyDetectionBenchmarking |here]]).

I have also been leading a "show-case" on content-based copy detection with the objective to present user-oriented demos in professional and scientific meetings (more info [[ http://www.muscle-noe.org/content/view/148/64/|here]]).

You can watch '''THE MOVIE''' :
||border=0 width=70%
|| [[ftp://ftp.inria.fr/scratch/multimedia/MUSCLE/imedia-visualcopyretrieval.mov|http://www-rocq.inria.fr/~ajoly/quicktime.jpeg]]  || [[ftp://ftp.inria.fr/scratch/multimedia/MUSCLE/visualcopyretrieval_final_384kbit%2016-9INRIA.rm|http://www-rocq.inria.fr/~ajoly/realplayer.jpeg]]  || [[http://www.dailymotion.com/relevance/search/imedia%2Bcopy/video/x4e0gp_the-visual-copy-retrieval_tech|http://www-rocq.inria.fr/~ajoly/dailymotion.jpeg]]||

! Visual Local Features
* Constant tangential angle interest points [[http://www-rocq.inria.fr/~arebai/papers/07/civr.pdf| [CIVR07] ]]
We developped with my master student, Ahmed Rebai, a new symmetry oriented interest point detector based on gradient orientations convergence. The aim was to reach better visual saliency than current detectors, such as Harris or Difference of Gaussians points. The developed technique gives very promising object class recognition performances.

%center% http://www-rocq.inria.fr/~ajoly/watch.jpg

* Dissociated dipoles [[http://portal.acm.org/citation.cfm?id=1282280.1282362| [DIPOLES07] ]]
I have also been working on new local descriptors dedicated to transformed images retrieval, with a small dimension (20) and a high discrimination power. These descriptors obtained the best results in [[http://www.imageval.org/resultats.html|ImagEval benchmark]] both in term of recall/precision and search rapidity.

%center% http://www-rocq.inria.fr/~ajoly/dipoles.jpg

! Density-based selection of local features [[http://www-rocq.inria.fr/~ajoly/ajolyMIR05vf.pdf| [MIR05] ]]
'''Keywords''': image retrieval, local features, discriminant, density estimation\\
This work started in collaboration with the NII (National Institute of Japan) within the scope of my visit in Tokyo (july 2005).
Local features are well-suited to content-based image retrieval because of their locality, their local uniqueness and their high information content [[#Miko05 | [4] ]]. However, as they are selected only according to the local information content in the image, there is no guaranty that they will be distinctive in a large set of images. A local feature corresponding to a high saliency in the image can be highly redundant in some specific databases, such as the TV news database stored at NII in which textual characters are extremely frequent. To overcome this issue, we propose [[#jolymir05| [5] ]] to select relevant local features directly according to their discrimination power in a specific set of images. By computing the density of the local features in a source database with a new fast non parametric density estimation technique, it is indeed possible to select quickly the most ''rare'' local features in a large set of images. Figure illustrates the difference between the 20 most salient points of an image and the 20 most rare points according to their density in a large image database. Currently, we are also looking at selecting local features according to their density in a single image or in a class of images, as done for textual features with TF/IDF techniques.

%center% %width=260% http://www-rocq.inria.fr/~ajoly/images/femme_harris648.jpg http://www-rocq.inria.fr/~ajoly/images/femme_rares648.jpg
%center% '''left: 20 most salient points - right: 20 most rare points'''

[[#Dork03]][1] "Selection of Scale-Invariant Parts for Object Class Recognition", G. Dorko, C. Schmid, IEEE Int. Conf. on Computer Vision, vol. 1, pp. 634--640, 2003.\\
[[#low04]][2] "Distinctive image features from scale-invariant keypoints", D. Lowe, Int. Journal of Computer Vision, vol. 60, no. 2, pp. 91--110, 2004.\\
[[#joly05]][[http://www-rocq.inria.fr/~ajoly/ajolyICIP05.pdf| [3] ]] "Content-based video copy detection in large databases: A local fingerprints  statistical similarity search approach", A. Joly, C. Frélicot and O. Buisson, in Proceedings of the Int. Conf. on Image Processing, 2005.\\
[[#Miko05]][4] K. Mikolajczyk, C. Schmid. "A performance evaluation of local descriptors," cvpr, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. 1615--1630, 2005.\\
[[#jolymir05]][[http://www-rocq.inria.fr/~ajoly/ajolyMIR05vf.pdf| [5] ]] "Discriminant Local Features Selection using Efficient Density Estimation in a Large Database", A. Joly and O. Buisson, ACM Int. Workshop on Multimedia Information Retrieval, invited paper, 2005.
to:
[[[Researsh Works]]]

[[[Projects]]]

[[[Benchmarking]]]
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! Density-based selection of local features [[http://www-rocq.inria.fr/~ajoly/ajolyMIR05vf.pdf| [MIR05] ]]
'''Keywords''': image retrieval, local features, discriminant, density estimation\\
This work started in collaboration with the NII (National Institute of Japan) within the scope of my visit in Tokyo (july 2005).
Local features are well-suited to content-based image retrieval because of their locality, their local uniqueness and their high information content [[#Miko05 | [4] ]]. However, as they are selected only according to the local information content in the image, there is no guaranty that they will be distinctive in a large set of images. A local feature corresponding to a high saliency in the image can be highly redundant in some specific databases, such as the TV news database stored at NII in which textual characters are extremely frequent. To overcome this issue, we propose [[#jolymir05| [5] ]] to select relevant local features directly according to their discrimination power in a specific set of images. By computing the density of the local features in a source database with a new fast non parametric density estimation technique, it is indeed possible to select quickly the most ''rare'' local features in a large set of images. Figure illustrates the difference between the 20 most salient points of an image and the 20 most rare points according to their density in a large image database. Currently, we are also looking at selecting local features according to their density in a single image or in a class of images, as done for textual features with TF/IDF techniques.

%center% %width=260% http://www-rocq.inria.fr/~ajoly/images/femme_harris648.jpg http://www-rocq.inria.fr/~ajoly/images/femme_rares648.jpg
%center% '''left: 20 most salient points - right: 20 most rare points'''

[[#Dork03]][1] "Selection of Scale-Invariant Parts for Object Class Recognition", G. Dorko, C. Schmid, IEEE Int. Conf. on Computer Vision, vol. 1, pp. 634--640, 2003.\\
[[#low04]][2] "Distinctive image features from scale-invariant keypoints", D. Lowe, Int. Journal of Computer Vision, vol. 60, no. 2, pp. 91--110, 2004.\\
[[#joly05]][[http://www-rocq.inria.fr/~ajoly/ajolyICIP05.pdf| [3] ]] "Content-based video copy detection in large databases: A local fingerprints  statistical similarity search approach", A. Joly, C. Frélicot and O. Buisson, in Proceedings of the Int. Conf. on Image Processing, 2005.\\
[[#Miko05]][4] K. Mikolajczyk, C. Schmid. "A performance evaluation of local descriptors," cvpr, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. 1615--1630, 2005.\\
[[#jolymir05]][[http://www-rocq.inria.fr/~ajoly/ajolyMIR05vf.pdf| [5] ]] "Discriminant Local Features Selection using Efficient Density Estimation in a Large Database", A. Joly and O. Buisson, ACM Int. Workshop on Multimedia Information Retrieval, invited paper, 2005.
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! Logo retrieval with a contrario visual query expansion [[http://www-rocq.inria.fr/~ajoly/pmh-revised.pdf | [ACM09] ]]


NEW!! Go to BelgaLogos home page


! Interactive objects retrieval with efficient boosting [[http://www-rocq.inria.fr/~ajoly/pmh-revised.pdf | [ACM09] ]]


! Multi-probe locality sensitive hashing [[http://www-rocq.inria.fr/~ajoly/pmh-revised.pdf | [ACM08] ]]
We developed, jointly with Olivier Buisson at INA, a new similarity search structure dedicated to high dimensional features. Multi-probe LSH is built on the well-known LSH technique, but it intelligently probes multiple buckets that are likely to contain query results in a hash table. Our method is inspired by our previous work on probabilistic similarity search structures and improves upon
recent theoretical work on multi-probe and query adaptive
LSH. Whereas these methods are based on likelihood criteria
that a given bucket contains query results, we define a more
reliable a posteriori model taking account some prior about
the queries and the searched objects. This prior knowledge
allows a better quality control of the search and a more
accurate selection of the most probable buckets. We implemented a nearest neighbors search based on this paradigm
and performed experiments on different real visual features
datasets. We show that our a posteriori scheme outperforms
other multi-probe LSH while offering a better quality control. Comparisons to the basic LSH technique show that our
method allows consistent improvements both in space and
time efficiency.


! Content-based Video Copy Detection
The last research paper somehow summarizing my work on this topic: [[http://www-rocq.inria.fr/~ajoly/ajolyTMA07.pdf| [TMA07] ]]

I am currently less involved on research issues about CBCD but still strongly implied in benchmarking (more info [[http://www-rocq.inria.fr/~ajoly/index.php?n=Main.ContentBasedCopyDetectionBenchmarking |here]]).

I have also been leading a "show-case" on content-based copy detection with the objective to present user-oriented demos in professional and scientific meetings (more info [[ http://www.muscle-noe.org/content/view/148/64/|here]]).

You can watch '''THE MOVIE''' :
||border=0 width=70%
|| [[ftp://ftp.inria.fr/scratch/multimedia/MUSCLE/imedia-visualcopyretrieval.mov|http://www-rocq.inria.fr/~ajoly/quicktime.jpeg]]  || [[ftp://ftp.inria.fr/scratch/multimedia/MUSCLE/visualcopyretrieval_final_384kbit%2016-9INRIA.rm|http://www-rocq.inria.fr/~ajoly/realplayer.jpeg]]  || [[http://www.dailymotion.com/relevance/search/imedia%2Bcopy/video/x4e0gp_the-visual-copy-retrieval_tech|http://www-rocq.inria.fr/~ajoly/dailymotion.jpeg]]||
           


! Visual Local Features
* Constant tangential angle interest points [[http://www-rocq.inria.fr/~arebai/papers/07/civr.pdf| [CIVR07] ]]
We developped with my master student, Ahmed Rebai, a new symmetry oriented interest point detector based on gradient orientations convergence. The aim was to reach better visual saliency than current detectors, such as Harris or Difference of Gaussians points. The developed technique gives very promising object class recognition performances.

%center% http://www-rocq.inria.fr/~ajoly/watch.jpg

* Dissociated dipoles [[http://portal.acm.org/citation.cfm?id=1282280.1282362| [DIPOLES07] ]]
I have also been working on new local descriptors dedicated to transformed images retrieval, with a small dimension (20) and a high discrimination power. These descriptors obtained the best results in [[http://www.imageval.org/resultats.html|ImagEval benchmark]] both in term of recall/precision and search rapidity.

%center% http://www-rocq.inria.fr/~ajoly/dipoles.jpg


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* [+[[Main.MyPhdResearchActivitiesAtINA|My Phd at INA]]+]
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* '''[+VITALAS+]''': European Integrated project concerning "Search Engines for Audio-Visual Content"
** WP 2 '''leader''': "Enabling technologies: media content description and scalability issues"
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* '''[+INFOMAGIC+]''':  This 3 years french project relates to the integration and the assesment of the best regional technologies in knowledge engineering. I am currently coordinating IMEDIA's activities in this project.
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* [+[[http://www.imageval.org/ | ImagEval benchmark]]+]:  This project relates to the evaluation of technologies of image filtering, content-based image retrieval (CBIR) and automatic description of images in large-scale image databases. The objective of IMAGEVAL is double: to organize important evaluations starting from concrete needs and to evaluate technologies held by national and foreign research laboratories, and software solutions. I am currently coordinating this benchmark within the imedia team. IMEDIA ranked first in the three tasks we participated in. I was fully involved in Task1 (Transformed image recognition) and Task 4 (Object Recognition).
    '''[+See IMEDIA results+]''' [[http://www.imageval.org/resultats.html|HERE]]

* [+[[http://www.muscle-noe.org/component/option,com_frontpage/Itemid,1/ | MUSCLE Network of Excellence]]+] Multimedia Understanding through semabtics, Computation and Learning
** [[http://www-rocq.inria.fr/imedia/Muscle/WP5/Eteam_Saliency/index.html|Visual saliency e-team]] leader
** [[http://www.muscle-noe.org/content/blogcategory/19/64/|content-based copy detection show-case]] leader
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* [+[[http://www.imageval.org/ | ImagEval benchmark]]+]:  This project relates to the evaluation of technologies of image filtering, content-based image retrieval (CBIR) and automatic description of images in large-scale image databases. The objective of IMAGEVAL is double: to organize important evaluations starting from concrete needs and to evaluate technologies held by national and foreign research laboratories, and software solutions. I am currently coordinating this benchmark within the imedia team. IMEDIA ranked first in the three tasks we participated in. I was fully involved in Task1 (Transformed image recognition) and Task 4 (Object Recognition)

'''[+See IMEDIA results+]''' [[http://www.imageval.org/resultats.html|HERE]]
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* [+[[http://www.imageval.org/ | ImagEval benchmark]]+]:  This project relates to the evaluation of technologies of image filtering, content-based image retrieval (CBIR) and automatic description of images in large-scale image databases. The objective of IMAGEVAL is double: to organize important evaluations starting from concrete needs and to evaluate technologies held by national and foreign research laboratories, and software solutions. I am currently coordinating this benchmark within the imedia team. IMEDIA ranked first in the three tasks we participated in. I was fully involved in Task1 (Transformed image recognition) and Task 4 (Object Recognition).

        '''[+See IMEDIA results+]''' [[http://www.imageval.org/resultats.html|HERE]]
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* [+[[http://www.imageval.org/ | ImagEval benchmark]]+]:  This project relates to the evaluation of technologies of image filtering, content-based image retrieval (CBIR) and automatic description of images in large-scale image databases. The objective of IMAGEVAL is double: to organize important evaluations starting from concrete needs and to evaluate technologies held by national and foreign research laboratories, and software solutions. I am currently coordinating this benchmark within the imedia team.
IMEDIA ranked first over the three tasks we participated in.
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* [+[[http://www.imageval.org/ | ImagEval benchmark]]+]:  This project relates to the evaluation of technologies of image filtering, content-based image retrieval (CBIR) and automatic description of images in large-scale image databases. The objective of IMAGEVAL is double: to organize important evaluations starting from concrete needs and to evaluate technologies held by national and foreign research laboratories, and software solutions. I am currently coordinating this benchmark within the imedia team. IMEDIA ranked first in the three tasks we participated in. I was fully involved in Task1 (Transformed image recognition) and Task 4 (Object Recognition)
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* [+[[http://www.imageval.org/ | ImagEval benchmark]]+]:  This project relates to the evaluation of technologies of image filtering, content-based image retrieval (CBIR) and automatic description of images in large-scale image databases. The objective of IMAGEVAL is double: to organize important evaluations starting from concrete needs and to evaluate technologies held by national and foreign research laboratories, and software solutions. I am currently coordinating this benchmark within the imedia team.||
||
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* [+[[http://www.imageval.org/ | ImagEval benchmark]]+]:  This project relates to the evaluation of technologies of image filtering, content-based image retrieval (CBIR) and automatic description of images in large-scale image databases. The objective of IMAGEVAL is double: to organize important evaluations starting from concrete needs and to evaluate technologies held by national and foreign research laboratories, and software solutions. I am currently coordinating this benchmark within the imedia team.
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* [+[[http://www.imageval.org/ | ImagEval benchmark]]+]:  This project relates to the evaluation of technologies of image filtering, content-based image retrieval (CBIR) and automatic description of images in large-scale image databases. The objective of IMAGEVAL is double: to organize important evaluations starting from concrete needs and to evaluate technologies held by national and foreign research laboratories, and software solutions. I am currently coordinating this benchmark within the imedia team.
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* [+[[http://www.imageval.org/ | ImagEval benchmark]]+]:  This project relates to the evaluation of technologies of image filtering, content-based image retrieval (CBIR) and automatic description of images in large-scale image databases. The objective of IMAGEVAL is double: to organize important evaluations starting from concrete needs and to evaluate technologies held by national and foreign research laboratories, and software solutions. I am currently coordinating this benchmark within the imedia team.||
||
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** [+[[My current research activities in the IMEDIA group]]+]

** [+[[My Phd research activities at INA]]+]

[+[[http://www.imageval.org/ | ImagEval benchmark]]+]:  This project relates to the evaluation of technologies of image filtering, content-based image retrieval (CBIR) and automatic description of images in large-scale image databases. The objective of IMAGEVAL is double: to organize important evaluations starting from concrete needs and to evaluate technologies held by national and foreign research laboratories, and software solutions. I am currently coordinating this benchmark within the imedia team.
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* [+[[My current research activities in the IMEDIA group]]+]

* [+[[My Phd research activities at INA]]+]

* [+[[http://www.imageval.org/ | ImagEval benchmark]]+]:  This project relates to the evaluation of technologies of image filtering, content-based image retrieval (CBIR) and automatic description of images in large-scale image databases. The objective of IMAGEVAL is double: to organize important evaluations starting from concrete needs and to evaluate technologies held by national and foreign research laboratories, and software solutions. I am currently coordinating this benchmark within the imedia team.
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[+[[http://www.muscle-noe.org/component/option,com_frontpage/Itemid,1/ | MUSCLE Network of Excellence]]+] Multimedia Understanding through semabtics, Computation and Learning
* [[http://www-rocq.inria.fr/imedia/Muscle/WP5/Eteam_Saliency/index.html|Visual saliency e-team]] leader
* [[http://www.muscle-noe.org/content/blogcategory/19/64/|content-based copy detection show-case]] leader
to:
* [+[[http://www.muscle-noe.org/component/option,com_frontpage/Itemid,1/ | MUSCLE Network of Excellence]]+] Multimedia Understanding through semabtics, Computation and Learning
** [[http://www-rocq.inria.fr/imedia/Muscle/WP5/Eteam_Saliency/index.html|Visual saliency e-team]] leader
** [[http://www.muscle-noe.org/content/blogcategory/19/64/|content-based copy detection show-case]] leader
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December 13, 2006, at 05:58 PM by 192.93.2.16 -
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[+[[My current research activities in the IMEDIA group]]+]

[+[[My Phd research activities at INA]]+]
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** [+[[My current research activities in the IMEDIA group]]+]

** [+[[My Phd research activities at INA]]+]
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December 13, 2006, at 05:57 PM by 192.93.2.16 -
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'''[+See IMEDIA results+]''' [[HERE]]
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'''[+See IMEDIA results+]''' [[http://www.imageval.org/resultats.html|HERE]]
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'''[++See IMEDIA results++]''' [[HERE]]
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'''[+See IMEDIA results+]''' [[HERE]]
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'''[++See IMEDIA results++] [[HERE]]
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'''[++See IMEDIA results++]''' [[HERE]]
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IMEDIA ranked first over the three tasks we participated in.

'''[++See IMEDIA results++] [[HERE]]
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* [[content-based copy detection show-case]] leader
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* [[http://www.muscle-noe.org/content/blogcategory/19/64/|content-based copy detection show-case]] leader
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* [[http://www-rocq.inria.fr/imedia/Muscle/WP5/Eteam_Saliency/index.html|Visual saliency e-team]] '''leader'''
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* [[http://www-rocq.inria.fr/imedia/Muscle/WP5/Eteam_Saliency/index.html|Visual saliency e-team]] leader
* [[content-based copy detection show-case]] leader
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[+[[http://www.muscle-noe.org/component/option,com_frontpage/Itemid,1/ | MUSCLE]]+] '''[+Network of Excellence+]''' Multimedia Understanding through semabtics, Computation and Learning
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[+[[http://www.muscle-noe.org/component/option,com_frontpage/Itemid,1/ | MUSCLE Network of Excellence]]+] Multimedia Understanding through semabtics, Computation and Learning
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[+[[http://www.muscle-noe.org/component/option,com_frontpage/Itemid,1/ | MUSCLE Network of Excellence]]+]:
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[+[[http://www.muscle-noe.org/component/option,com_frontpage/Itemid,1/ | MUSCLE]]+] '''[+Network of Excellence+]''' Multimedia Understanding through semabtics, Computation and Learning
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* [http://www-rocq.inria.fr/imedia/Muscle/WP5/Eteam_Saliency/index.html|[Visual saliency e-team]] '''leader'''
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* [[http://www-rocq.inria.fr/imedia/Muscle/WP5/Eteam_Saliency/index.html|Visual saliency e-team]] '''leader'''
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* [[Visual saliency e-team]] '''leader'''
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* [http://www-rocq.inria.fr/imedia/Muscle/WP5/Eteam_Saliency/index.html|[Visual saliency e-team]] '''leader'''
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'''[+VITALAS project+]''': European Integrated project about "Search Engines for Audio-Visual Content"
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'''[+VITALAS project+]''': European Integrated project concerning "Search Engines for Audio-Visual Content"
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* [[Visual saliency e-team]] leader
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* [[Visual saliency e-team]] '''leader'''
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* WP 2 leader ("Enabling technologies: media content description and scalability issues")
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* WP 2 '''leader''': "Enabling technologies: media content description and scalability issues"
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* WP2 leader ("Enabling technologies: media content description and scalability issues")
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* WP 2 leader ("Enabling technologies: media content description and scalability issues")
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'''+[VITALAS project]+''': European Integrated project about "Search Engines for Audio-Visual Content"
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'''[+VITALAS project+]''': European Integrated project about "Search Engines for Audio-Visual Content"
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['''+VITALAS project+''']: European Integrated project about "Search Engines for Audio-Visual Content"
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[||+VITALAS project+||]: European Integrated project about "Search Engines for Audio-Visual Content"
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['''+VITALAS project+''']: European Integrated project about "Search Engines for Audio-Visual Content"
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[+VITALAS project+]: European Integrated project about "Search Engines for Audio-Visual Content"
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[+VITALAS project+]: European Integrated project about "Search Engines for Audio-Visual Content"
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[+[[http://www.muscle-noe.org/component/option,com_frontpage/Itemid,1/ | MUSCLE Network of Excellence]]+]: Visual saliency e-team leader, content-based copy detection show-case leader
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[+[[http://www.muscle-noe.org/component/option,com_frontpage/Itemid,1/ | MUSCLE Network of Excellence]]+]:
* [[Visual saliency e-team]] leader
* [[content-based copy detection show-case]] leader

[+[[VITALAS project]]+]: European Integrated project about "Search Engines for Audio-Visual Content"
* WP2 leader ("Enabling technologies: media content description and scalability issues")

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[+[[http://www.muscle-noe.org/component/option,com_frontpage/Itemid,1/ | MUSCLE Network of Excellence]]+]:  Participant to work package WP 5 on Content-based description and WP 7 on Computation intensive methods.
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[+[[http://www.muscle-noe.org/component/option,com_frontpage/Itemid,1/ | MUSCLE Network of Excellence]]+]: Visual saliency e-team leader, content-based copy detection show-case leader
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[+[[InfoMagic project]]+]:  This 3 years french project relates to the integration and the assesment of the best regional technologies in knowledge engineering. I am currently coordinating the InfoMagic activities within the imedia team.
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