2014.bib

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@inproceedings{AboubacarBL14,
  author = {Hattoibe Aboubacar and
	Vincent Barra and
	Gaëlle Loosli},
  title = {3D Shape Retrieval using Uncertain Semantic Query - A Preliminary Study},
  booktitle = {{ICPRAM} 2014 - Proceedings of the 3rd International Conference on
	Pattern Recognition Applications and Methods, ESEO, Angers, Loire
	Valley, France, 6-8 March, 2014},
  pages = {600--607},
  year = {2014},
  url = {https://doi.org/10.5220/0004819106000607},
  doi = {10.5220/0004819106000607},
  abstract = {The recent technological progress contributes to a huge increase of 3D models available in digital forms.
	Numerous applications were developed to deal with this amount of information, especially for 3D shape retrieval. One of 
	the main issues is to break the semantic gap between shapes desired by users and shapes returned by retrieval methods.
	In this paper, we propose an algorithm to address this issue. First the user gives a semantic request.
	Second,  a fuzzy 3D-shape generator sketches out suitable 3D-shapes. % by putting together simple shapes.
	Those shapes are filtered by the user or a learning machine to select the one that match the semantic query.  
	Then, we use a state-of-the-art retrieval method to return real-world 3D shapes that match this semantic query.
	We present results from an experiment. Three semantic concepts are learned and 3D shapes from SHREC'07 database 
	that match each concept are retrieved using our algorithm. The result are good and promising. }
}

@inproceedings{loosli2014using,
  title = {Using SVDD in SimpleMKL for 3D-Shapes Filtering},
  author = {Loosli, Gaëlle and Aboubacar, Hattoibe},
  booktitle = {CAp - 16eme Conférence d'apprentissage},
  pages = {84--92},
  year = {2014},
  abstract = {This paper proposes the adaptation of Support Vector Data Description (SVDD) to the multiple kernel case (MK-SVDD), based on SimpleMKL. It also introduces a variant called Slim-MK-SVDD that is able to produce  a tighter frontier around the data. For the sake of comparison, the equivalent methods are also developed for One-Class SVM, known to be very similar to SVDD for certain shapes of kernels. 
  Those algorithms are illustrated in the context of 3D-shapes filtering and outliers detection. For the 3D-shapes problem, the objective is to be able to select a sub-category of 3D-shapes, each sub-category being learned with our algorithm in order to create a filter. For outliers detection, we apply the proposed algorithms for unsupervised outliers detection as well as for the supervised case.  }
}

@article{ArouriNARBT14,
  author = {Cyrine Arouri and
	Engelbert Mephu Nguifo and
	Sabeur Aridhi and
	Cécile Roucelle and
	Gaëlle Bonnet{-}Loosli and
	Norbert Tsopzé},
  title = {Towards a constructive multilayer perceptron for regression task using
	non-parametric clustering. A case study of Photo-Z redshift reconstruction},
  journal = {CoRR},
  volume = {abs/1412.5513},
  year = {2014},
  url = {http://arxiv.org/abs/1412.5513},
  archiveprefix = {arXiv},
  eprint = {1412.5513},
  timestamp = {Fri, 08 Sep 2017 16:07:50 +0200}
}