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Object-centered feature selection for weakly-unsupervised object categorisation

We describe a novel approach of spatio-temporal mapping of local image features, to reduce the number of input data for further object categorization. The main focus of our work is the selection of good features to learn, by achieving a precise mapping of image features either related to static objects or to background. This can be done by initial camera motion estimation, subsequent structure estimation and final clustering of the 3D points. Experimental results show that our method achieves a significant reduction of processed image features, which yields a better performance in subsequent learning modules.