A framework that constructs an adaptive region similarity network from an initial segmentation using color and texture features and applies community detection algorithms to produce the final image segmentation, with tests on the Berkeley dataset showing improved performance.
Segmentation of large images based on super-pixels and community detection in graphs
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abstract
Image segmentation has many applications which range from machine learning to medical diagnosis. In this paper, we propose a framework for the segmentation of images based on super-pixels and algorithms for community identification in graphs. The super-pixel pre-segmentation step reduces the number of nodes in the graph, rendering the method the ability to process large images. Moreover, community detection algorithms provide more accurate segmentation than traditional approaches, such as those based on spectral graph partition. We also compare our method with two algorithms: a) the graph-based approach by Felzenszwalb and Huttenlocher and b) the contour-based method by Arbelaez. Results have shown that our method provides more precise segmentation and is faster than both of them.
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cs.CV 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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A General Framework for Complex Network-Based Image Segmentation
A framework that constructs an adaptive region similarity network from an initial segmentation using color and texture features and applies community detection algorithms to produce the final image segmentation, with tests on the Berkeley dataset showing improved performance.