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arXiv · 1804.02721

Image Segmentation using Sparse Subset Selection

Fariba Zohrizadeh·Mohsen Kheirandishfard·Farhad Kamangar
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Abstract

In this paper, we present a new image segmentation method based on the concept of sparse subset selection. Starting with an over-segmentation, we adopt local spectral histogram features to encode the visual information of the small segments into high-dimensional vectors, called superpixel features. Then, the superpixel features are fed into a novel convex model which efficiently leverages the features to group the superpixels into a proper number of coherent regions. Our model automatically determines the optimal number of coherent regions and superpixels assignment to shape final segments. To solve our model, we propose a numerical algorithm based on the alternating direction method of multipliers (ADMM), whose iterations consist of two highly parallelizable sub-problems. We show each sub-problem enjoys closed-form solution which makes the ADMM iterations computationally very efficient. Extensive experiments on benchmark image segmentation datasets demonstrate that our proposed method in combination with an over-segmentation can provide high quality and competitive results compared to the existing state-of-the-art methods.

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Fariba Zohrizadeh, Mohsen Kheirandishfard, Farhad Kamangar. 2018-04-08. Image Segmentation using Sparse Subset Selection. https://arxiv.org/abs/1804.02721

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