Segmentation of large images based on super-pixels and community detection in graphs
This work addresses image segmentation for applications like machine learning and medical diagnosis, but it appears incremental as it builds on existing super-pixel and graph-based techniques.
The paper tackles image segmentation by proposing a framework that uses super-pixels and community detection in graphs to process large images, resulting in more precise segmentation and faster performance compared to existing methods.
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.