Tuning SVM-Based Relevance Feedback for the Interactive Classification of Images

In the context of SVM-based interactive image classification with relevance feedback, we put forward a new active learning selection criterion that minimizes redundancy between the candidate images shown to the user at every round. We also show that insensitivity to the scale of the data is an important quality of the learning machine and we propose the use of specific kernel functions to achieve this. Experiments performed on several image databases confirms the atractiveness of our suggestions.