Learning to Detect Natural Image Boundaries Using Brightness and Texture

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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David Martin, Charless Fowlkes, Jitendra Malik


The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, a classi´Čüer is trained using human labeled images as ground truth. We present precision-recall curves showing that the resulting detector outperforms existing approaches.