Concurrent Object Recognition and Segmentation by Graph Partitioning

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

Bibtex Metadata Paper

Authors

Stella X. Yu, Ralph Gross, Jianbo Shi

Abstract

Segmentation and recognition have long been treated as two separate pro(cid:173) cesses. We propose a mechanism based on spectral graph partitioning that readily combine the two processes into one. A part-based recogni(cid:173) tion 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 in(cid:173) teractions 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 elimi(cid:173) nates false positives for the part detection, while overcoming occlusion and weak contours for the low-level edge detection.