Transductive inference & kernel design for object class segmentation

Transductive inference techniques are nowadays becoming standard in machine learning due to their relative success in solving many real-world applications. Among them, kernel-based methods are particularly interesting but their success remains highly dependent on the choice of kernels. The latter are usually handcrafted or designed in order to capture better similarity in training data. In this paper, we introduce a novel transductive learning algorithm for kernel design and classification. Our approach is based on the minimization of an energy function mixing i) a reconstruction term that factorizes a matrix of input data as a product of a learned dictionary and a learned kernel map ii) a fidelity term that ensures consistent label predictions with those provided in a ground-truth and iii) a smoothness term which guarantees similar labels for neighboring data and allows us to iteratively diffuse kernel maps and labels from labeled to unlabeled data. Solving this minimization problem makes it possible to learn both a decision criterion and a kernel map that guarantee linear separability in a high dimensional space and good generalization performance. Experiments conducted on object class segmentation, show improvements with respect to baseline as well as related work on the challenging VOC database.