This post-doc deals with detection and recognition of objects in images and video streams, on a large scale. This is a fundamental task that is the subject of active research in the world, including recent challenges in the evaluation campaigns. The "large scale" aspect refer to both large size databases (eg ten million images) and large number of concepts to recognize (eg 100-10000).The work will concern bothimage description and classification.
At the description level, state of the art techniques rely on local descriptors, aggregated according to dictionaries of "visual words" possibly constructed using Fisher kernels. It is nevertheless necessary to recode these signatures effectively in order to manage large databases. Regarding learning of visual concepts or objects, many algorithms use SVM (support vector machines) but other approaches are sometimes considered, such as those based on boosting or logistic regression.
The proposed position involves research and development of efficient algorithms to find visual entities in very large databases. Tracks are considered and should be discussed with the candidate selected based on prior knowledge and technical discussions.