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

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.
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Contributor : Simon Masnou <>
Submitted on : Friday, July 26, 2013 - 1:48:05 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:14 PM
Long-term archiving on: : Sunday, October 27, 2013 - 2:50:10 AM


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Xiaofang Wang, Huibin Li, Simon Masnou, Liming Chen. Sparse Coding and Mid-Level Superpixel-Feature for l0-Graph Based Unsupervised Image Segmentation. Computer Analysis of Images and Patterns, 2013, United Kingdom. pp.160-168, ⟨10.1007/978-3-642-40246-3_20⟩. ⟨hal-00833275⟩



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