10 April 2018 Weakly supervised image semantic segmentation based on clustering superpixels
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106151Y (2018) https://doi.org/10.1117/12.2302479
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
In this paper, we propose an image semantic segmentation model which is trained from image-level labeled images. The proposed model starts with superpixel segmenting, and features of the superpixels are extracted by trained CNN. We introduce a superpixel-based graph followed by applying the graph partition method to group correlated superpixels into clusters. For the acquisition of inter-label correlations between the image-level labels in dataset, we not only utilize label co-occurrence statistics but also exploit visual contextual cues simultaneously. At last, we formulate the task of mapping appropriate image-level labels to the detected clusters as a problem of convex minimization. Experimental results on MSRC-21 dataset and LableMe dataset show that the proposed method has a better performance than most of the weakly supervised methods and is even comparable to fully supervised methods.
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Xiong Yan, Xiong Yan, Xiaohua Liu, Xiaohua Liu, } "Weakly supervised image semantic segmentation based on clustering superpixels", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151Y (10 April 2018); doi: 10.1117/12.2302479; https://doi.org/10.1117/12.2302479
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