Unsupervised Multi-Class Co-Segmentation via Joint-Cut Over L1 -Manifold Hyper-Graph of Discriminati

Abstract

This paper systematically advocates a robust and efficient unsupervised multi-class co-segmentation approach by leveraging underlying subspace manifold propagation to exploit the cross-image coherency. It can combat certain image cosegmentation difficulties due to viewpoint change, partial occlusion, complex background, transient illumination, and cluttering texture patterns. Our key idea is to construct a powerful hyper-graph joint-cut framework, which incorporates mid-level image regions based intra-image feature representation and L1- manifold graph based inter-image coherency exploration. For local image region generation, we propose a bi-harmonic distance distribution difference metric to govern the super-pixel clustering in a bottom-up way. It not only affords drastic data reduction but also gives rise to discriminative and structure-meaningful feature representation. As for the inter-image coherency, we leverage multi-type features involved L1-graph to detect the underlying local manifold from cross-image regions. As a result, the implicit supervising information could be encoded into the unsupervised hyper-graph joint-cut framework. We conduct extensive experiments and make comprehensive evaluations with other stateof- the-art methods over various benchmarks, including iCoseg, MSRC, and Oxford flower. All the results demonstrate the superiorities of our method in terms of accuracy, robustness, efficiency, and versatility.

Existing System

Proposed System

Architecture