An Iterative Combination Scheme for multimodal visual feature detection

We address the problem of multimodal visual feature detection where several individual heterogeneous measures (i.e., feature detectors) are merged into a single saliency value. A newapproach, the Iterative Combination Scheme, is proposed to iteratively learn a classifier that infers a non-linear model to combine different feature detectors. We evaluate and compare the combination strategies presented using an objective methodology, the repeatability criterion, and a dataset with real images of 21 cluttered scenes of 3D objects.