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Hierarchy and adaptivity in segmenting visual scenes

Abstract

Finding salient, coherent regions in images is the basis for many visual tasks, and is especially important for object recognition. Human observers perform this task with ease, relying on a system in which hierarchical processing seems to have a critical role1. Despite many attempts, computerized algorithms2,3,4,5 have so far not demonstrated robust segmentation capabilities under general viewing conditions. Here we describe a new, highly efficient approach that determines all salient regions of an image and builds them into a hierarchical structure. Our algorithm, segmentation by weighted aggregation, is derived from algebraic multigrid solvers for physical systems6, and consists of fine-to-coarse pixel aggregation. Aggregates of various sizes, which may or may not overlap, are revealed as salient, without predetermining their number or scale. Results using this algorithm are markedly more accurate and significantly faster (linear in data size) than previous approaches.

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Figure 1: SWA.
Figure 2: The multiscale normalized cut graph approach.
Figure 3: Segmentation results for eight challenging images of animals on cluttered backgrounds.
Figure 4: Similarity search by parts.

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Acknowledgements

Research was supported in part by the European Commission Project Aim Shape, the Binational Science foundation, and by the German–Israeli Foundation. D.S. was supported by a grant from the National Institutes of Health. The research was conducted at the Moross Laboratory for Vision and Motor Control at the Weizmann Institute of Science. We thank N. Rubin and D. Jacobs for many useful remarks, and S. Geman for commenting on an earlier version of the manuscript. We are grateful to E. Borenstein for his help with constructing the sunglasses search system. We also thank M. Varma and R. Deitch for help with the comparisons presented in the Supplementary Information and N. Brandt for help with the graphics.

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Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests. Correspondence and requests for materials should be addressed to E.S. (eitan.sharon@weizmann.ac.il).

Supplementary information

Supplementary Figure 1

Adaptive vs. geometric aggregation. (PDF 24 kb)

Supplementary Figure 2

Comparison of results of recently published segmentation methods for eight challenging images of animals on cluttered backgrounds. (PDF 1321 kb)

Supplementary Figure 3

The full sunglasses database. (PDF 13953 kb)

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Sharon, E., Galun, M., Sharon, D. et al. Hierarchy and adaptivity in segmenting visual scenes. Nature 442, 810–813 (2006). https://doi.org/10.1038/nature04977

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