Abstract
The task of hierarchical image segmentation attempts to parse images from coarse to fine and provides a structural configuration by the output of a tree-like structure. To deal with the challenges of keeping semantic consistency in each level caused by the variable scale of different objects in image, this paper proposes a hierarchical image segmentation approach guided by multi-feature fusion and energy optimization. We transform the image into a region adjacency graph (RAG) by superpixels and design a bottom-up progressive merging framework based on graph cut for a hierarchical region tree. A multiscale structural edge is designed as a feature map for mapping to the hierarchical levels, while we conduct salient map and object window as a weakly-supervised prior during the optimization process. Experimental results demonstrate that our approach gets a better performance in semantic consistency while has an encouraging performance compared with some state-of-the-arts.
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Acknowledgments
This work was supported by National High Technology Re-search and Development Program of China (No. 2007AA01Z334), National Natural Science Foundation of China (Nos. 61321491 and 61272219), Program for New Century Excellent Talents in University of China (NCET-04-04605), the China Postdoctoral Science Foundation (Grant No. 2017M621700) and Innovation Fund of State Key Lab for Novel Software Technology (Nos. ZZKT2013A12, ZZKT2016A11 and ZZKT2018A09).
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Hu, A., Sun, Z., Guo, Y., Li, Q. (2018). Hierarchical Image Segmentation Based on Multi-feature Fusion and Graph Cut Optimization. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_55
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DOI: https://doi.org/10.1007/978-3-030-00767-6_55
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