Image collection structuring based on evidential active learner

Organising a collection of images requires an intensive and time consuming human effort. We present here a framework to classify dynamically collections of images without a priori content knowledge. Our work is based on active learning techniques: unlabeled samples are selected iteratively one by one, and a knn-evidential classifier make a proposition of label at each step. Users can initialize, remove or merge classes and may correct the propositions. The Transferable Belief Model framework offers us a complete formal model to express jointly the classifier and different sampling strategies such as positivity, ambiguity and diversity. Our aims are to study these different sampling strategies in order to minimize the error rates as well as the user cognitive charge according to the distribution of the endeavor over time.