PhD position at LIG research lab, Grenoble, France

Application deadline: 30 September 2009.

Starting date: 1st November or 1st December 2009.

Supervisors: Georges Quénot (Researcher at CNRS)

Philippe Mulhem (Researcher at CNRS)

Contact: Georges.Quenot@imag.fr, Philippe.Mulhem@imag.fr.

Title: Using context for semantic indexing of image and video documents

The automated document indexing image and video is a difficult problem

because of the “distance” between the arrays of numbers encoding these

documents and the concepts (e.g. people, places, events or objects)

with which we wish to annotate them. Methods exist for this but their

results are far from satisfactory in terms of generality and accuracy.

They generally operate by supervised or semi-supervised learning: the

system learns to recognize concepts from positive and negative examples;

it “generalizes” from these examples. Existing methods typically use a

single set of such examples and consider it as uniform. This is not

optimal because the same concept may appear in various contexts and its

appearance may be very different depending upon these contexts. The

context may be: the type of broadcast (television news, fiction,

entertainment, advertising, etc.), Date, place, country or culture of

broadcasting or production, or the modalities present or absent (for

documents in black and white and / or without sound, for instance).

The context may generally be regarded as another concept or as a set

of other concepts. The concepts and relations between them can be

represented in ontologies. One can interpret the relationship within

an ontology like the fact that the elements are likely to be together

or not in an image or a in a video shot and this information can be

used for their automatic annotation.

The proposed subject concerns the use of the context to improve the

performance of classifiers. The main idea is to consider, for each

concept to be recognized, a number of contexts in which it may appear

and to train a classifier for each of these contexts. During the

recognition, the appropriate classifier is used according to the

identified context. Alternatively, a weighted combination (fusion)

of classification results can be used if we only have probabilities

of being in a given context. Such an approach presents several

difficulties. The first one is the identification of context during

the recognition: in some cases, it may be known explicitly (from

metadata, for example) but, in general, it is actually another

concept, which also has to be recognized. The second difficulty is

the need for a very important total volume of training data so that,

for each context, there are enough examples to properly train a

classifier. There is a complexity that is related to simultaneously

manage the tuning of multiple classifiers for each concept. The

third difficulty concerns the problem of merging the outputs of

different classifiers in the frequent case in which there are

uncertainties about the context actually present during the

recognition. Implementation will possibly be based on the use of

network operators (extractors of features, classifiers and merge

modules), of ontologies to manage relationships between concepts

and of active learning for automatic training data collection.

The developed methods will be evaluated in the context of national

and international campaigns like TRECVID

(http://www-nlpir.nist.gov/projects/trecvid/). The work will be

done in the context of the Quaero program (http://www. quaero.org).

This will, among other things, give access to a large volume of

annotated image and video data.

http://mrim.imag.fr/georges.quenot/postes/These-Quaero-2-FR.pdf

http://mrim.imag.fr/georges.quenot/postes/These-Quaero-2-EN.pdf

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