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Concurrent Object Recognition and Segmentation by Graph Partitioning
- Stella X. YU, Ralph GROSS and Jianbo SHI
Neural Information Processing Systems ( NIPS ), Vancouver, Canada,
Dec 9-14, 2002.
- Abstract
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Segmentation and recognition have long been treated as two separate
processes. We propose a mechanism based on spectral graph
partitioning that readily combine the two processes into one. A
part-based recognition system detects object patches, supplies their
partial segmentations as well as knowledge about the spatial
configurations of the object. The goal of patch grouping is to find
a set of patches that conform best to the object configuration,
while the goal of pixel grouping is to find a set of pixels that
have the best low-level feature similarity. Through
pixel-patch interactions and between-patch competition encoded in
the solution space, these two processes are realized in one joint
optimization problem. The globally optimal partition is obtained by
solving a constrained eigenvalue problem. We demonstrate that the
resulting object segmentation eliminates false positives for the
part detection, while overcoming occlusion and weak contours for the
low-level edge detection.
- Keywords
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grouping,
image segmentation,
object recognition,
part detection,
figure-ground,
graph partitioning,
bias, attention.
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