Contour Detection and Image Segmentation

Contour Detection and Image Segmentation by Michael Randolph Maire Doctor of Philosophy in Computer Science University of California, Berkeley Professor Jitendra Malik, Chair This thesis investigates two fundamental problems in computer vision: contour detection and image segmentation. We present new state-of-the-art algorithms for both of these tasks. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. Our approach to contour detection couples multiscale local brightness, color, and texture cues to a powerful globalization framework using spectral clustering. The local cues, computed by applying oriented gradient operators at every location in the image, define an affinity matrix representing the similarity between pixels. From this matrix, we derive a generalized eigenproblem and solve for a fixed number of eigenvectors which encode contour information. Using a classifier to recombine this signal with the local cues, we obtain a large improvement over alternative globalization schemes built on top of similar cues. To produce high-quality image segmentations, we link this contour detector with a generic grouping algorithm consisting of two steps. First, we introduce a new im-