The LEAR research group at INRIA Grenoble and the TVPA research group
at the Xerox Research Center Europe in Grenoble are looking for a PhD
student. The candidate will be jointly supervised and will spend time
in both institutions
(
http://lear.inrialpes.fr/;
http://www.xrce.xerox.com).
Topic:
Annotated data has in the past been viewed as a scarce resource for
image classification. However, this perception is changing as large
amounts of labeled images are becoming available [1]. As a
consequence, the focus is shifting from "how to learn a category from
a single image?" [2] to "how to handle large quantities of
data?" [5,6,7,8,11,12]. The focus of this PhD will be image
classification on a large scale, i.e. in the case of millions of
images and thousands of classes.
This PhD will improve the accuracy of image classifiers on a large
scale. A possible solution is to use structured output learning for
large hierarchies of semantic classes. This has shown to be successful
for pattern recognition problems of moderate size [10]. However, it is
unclear how it scales to very large numbers of images and classes [5].
Another possible research line is the use of image segmentation. It
was shown that object segmentation can boost the
accuracy of image classification [3]. However, this comes at a
significant cost which is incompatible with large-scale datasets.
Hence, efficient alternatives to complete segmentation have to be
developed. While the primary application of large scale
classification is image annotation, the availability of a large
number of accurate image classifiers can have other applications, such
as large-scale image retrieval [4], where semantic information is
necessary [9,12].
Your profile:
* Masters degree (preferably in Computer Science; Mathematics and
Electrical Engineering will also be considered)
* Solid programming skills; the projects involve programming in C/C
++ (and some Matlab)
* Solid mathematics knowledge (especially linear algebra and
statistics)
* Creative and highly motivated
* Fluent in English, both written and spoken
* Prior knowledge in the areas of computer vision, machine
learning or data mining is a plus (ideally a master thesis in a
related field)
Duration:
3 years.
Start date:
As soon as possible
Location:
This is a joint project between INRIA Grenoble and XRCE.
The two research centers are just minutes away from each other.
The candidate will be required to spend time in both institutions.
Contacts:
Dr. Cordelia Schmid, sch...@inrialpes.fr
Dr. Florent Perronnin, florent.perron...@xrce.xerox.com
Please send applications via email, including:
* a complete CV
* graduation marks
* topic of your master thesis
* the name and email address of one reference (preferably your
master thesis supervisor)
* if you already have research experience, please include a
publication list and references.
Literature:
[1] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-
Fei. Imagenet: A large-scale hierarchical image database. In
CVPR, 2009.
[2] L. Fei-Fei, R. Fergus, and P. Perona. One-shot learning of
object categories. IEEE PAMI, 28(4), 2006.
[3] H. Harzallah, F. Jurie, C. Schmid. Combining efficient object
localization and image classification. In ICCV, 2009.
[4] H. Jegou, M. Douze, and C. Schmid. Hamming embedding
and weak geometric consistency for large scale image search.
In ECCV, 2008.
[5] Y. Li, D. Crandall, and D. Huttenlocher. Landmark classification
in large-scale image collections. In ICCV, 2009.
[6] S. Maji and A. Berg. Max-margin additive classifiers for
detection. In ICCV, 2009.
[7] F. Perronnin, J. Sanchez and Y. Liu. Large-scale image
categorization with explicit data embedding. In CVPR, 2010.
[8] F. Perronnin, J. Sanchez and T. Mensink. Improving the Fisher
kernel for large-scale image classification. In ECCV, 2010.
[9] N. Rasiwasia, P. Moreno, and N. Vasconcelos. Bridging the gap:
Query by semantic example. IEEE Trans. on MM, 9(5):923--938, 2007.
[10] I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun.
Support vector machine learning for interdependent and
structured output spaces. In ICML, 2004.
[11] A. Vedaldi and A. Zisserman. Efficient additive kernels via
explicit feature maps. In. CVPR, 2010.
[12] G. Wang, D. Hoiem, and D. Forsyth. Learning image similarity
from flickr groups using stochastic intersection kernel
machines. In ICCV, 2009.
