Sparse Coding and Mid-Level Superpixel-Feature for ℓ0-Graph Based Unsupervised Image Segmentation

  • Xiaofang Wang
  • Huibin Li
  • Simon Masnou
  • Liming Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8048)

Abstract

We propose in this paper a graph-based unsupervised segmentation approach that combines superpixels, sparse representation, and a new mid-level feature to describe superpixels. Given an input image, we first extract a set of interest points either by sampling or using a local feature detector, and we compute a set of low-level features associated with the patches centered at the interest points. We define a low-level dictionary as the collection of all these low-level features. We call superpixel a region of an oversegmented image obtained from the input image, and we compute the low-level features associated with it. Then we compute for each superpixel a mid-level feature defined as the sparse coding of its low-level features in the aforementioned dictionary. These mid-level features not only carry the same information as the initial low-level features, but also carry additional contextual cue. We use the superpixels at several segmentation scales, their associated mid-level features, and the sparse representation coefficients to build graphs at several scales. Merging these graphs leads to a bipartite graph that can be partitioned using the Transfer Cut algorithm. We validate the proposed mid-level feature framework on the MSRC dataset, and the segmented results show improvements from both qualitative and quantitative viewpoints compared with other state-of-the-art methods.

Keywords

image segmentation sparse coding superpixels mid-level features 0-graph 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaofang Wang
    • 1
  • Huibin Li
    • 1
  • Simon Masnou
    • 2
    • 3
  • Liming Chen
    • 1
  1. 1.LIRIS UMR5205Ecole Centrale de LyonFrance
  2. 2.CNRS UMR 5208Université de LyonFrance
  3. 3.Institut Camille JordanUniversité Lyon 1Villeurbanne cedexFrance

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