Concurrent Object Recognition and Segmentation by Graph Partitioning

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.