Departmental Papers (CIS)

Document Type

Conference Paper

Date of this Version

June 2005

Comments

Copyright 2005 IEEE. Reprinted from Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005 (CVPR 2005), Volume 2, pages 1124-1131.

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Abstract

We present a multiscale spectral image segmentation algorithm. In contrast to most multiscale image processing, this algorithm works on multiple scales of the image in parallel, without iteration, to capture both coarse and fine level details. The algorithm is computationally efficient, allowing to segment large images. We use the Normalized Cut graph partitioning framework of image segmentation. We construct a graph encoding pairwise pixel affinity, and partition the graph for image segmentation.We demonstrate that large image graphs can be compressed into multiple scales capturing image structure at increasingly large neighborhood. We show that the decomposition of the image segmentation graph into different scales can be determined by ecological statistics on the image grouping cues. Our segmentation algorithm works simultaneously across the graph scales, with an inter-scale constraint to ensure communication and consistency between the segmentations at each scale. As the results show, we incorporate long-range connections with linear-time complexity, providing high-quality segmentations efficiently. Images that previously could not be processed because of their size have been accurately segmented thanks to this method.

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Date Posted: 25 February 2006