CONTEXTUAL KERNEL MAP LEARNING FOR SCENE TRANSDUCTION

Scene understanding, also known as object category segmentation, is one of the major trends in computer vision. It consists in modeling and inferring object categories through constellations of pixels belonging to a given test image. Many existing solutions suffer from (at least) two major limitations; on the one hand, they only use few (scarce) labeled training data, and on the other hand they rely on context-free learning models, thereby, their potential is not fully explored. In this paper, we adopt a transductive data-driven approach for scene understanding based on kernel machines. The main contribution of this work includes i) a novel transductive approach that exploits both labeled/unlabeled data and jointly learns classifiers and kernel maps for better discrimination, and ii) a context modeling approach that captures semantic as well as geometric relationships between object categories as a part of kernel learning. Experiments conducted in scene understanding, using the SiftFlow dataset, show that the proposed method is competitive against state of the art.