Weakly Supervised Labeling of Dominant Image Regions in Indoor Sequences

The capability of associating semantic concepts with available sensory data is an important component of environment understanding. In this work we describe an approach for annotation of dominant image regions of uniform appearance, which are typically encountered indoors, such as doors, walls and floors. One of the main challenges behind correct classification of these regions requires handling large changes in the appearance as a function of lighting conditions. Instead of using large amount of training data taken under different illumination conditions, we propose an online updating of the model learned from a small number of training examples in the initial frame. We follow a two stage classification strategy: first we estimate the probabilities of individual regions belonging to each class based on appearance only; in the second stage we use Markov Random Fields (MRF) to exploit spatial layout of the scene and improve classification results. The appearance model learned in the first frame is updated in subsequent frames using the confidences obtained by the two stage classification strategy. We demonstrate our approach on two sequences of indoor environments.