Treffer: Scene Recognition using Visual Attention, Invariant Local Features and Visual Landmarks

We consider the task of scene recognition, in the context of a robot-like navigation application, using a visual attention model of bottom-up saliency, invariant local features and visual landmarks, and the Nearest Neighbor rule for classificatiort. Experimental work shows that important reductions in the number of prototypes used by the NN classifier can be achieved using saliency maps. We also present a novel approach to extract visual landmarks that uses the model of bottom-up saliency to localize interest points, and color centiles plus local binary pattern histograms to collect local description of them. In the experiments, this later approach outperforms SIFT features by achieving similar recognition results but further reductions in the size of the database of prototypes,thus providing bigger savings in computational costs.