https://scholars.lib.ntu.edu.tw/handle/123456789/607205
Title: | Learning Based Superpixel Merging Model for Image Segmentation | Authors: | Huang J.-Y Ding J.-J Huang P.-C. JIAN-JIUN DING |
Keywords: | Computer vision;Deep learning;Image segmentation;Superpixel merging;Merging;Superpixels;Convolutional networks;Images segmentations;Irregular shape;Network-based;Segmentation algorithms;Segmentation methods;Segmentation results;Super pixels | Issue Date: | 2021 | Journal Volume: | 2021-August | Start page/Pages: | 661-665 | Source: | European Signal Processing Conference | Abstract: | Most conventional segmentation methods are superpixel-based. Recently, the convolutional network (CNN) has been adopted in image segmentation. However, most existing CNN-based segmentation algorithms are pixel-wise. Due to the irregular shape and the non-fixed size of superpixels, it is hard to apply superpixels into the CNN architecture directly. In this work, several ideas are proposed to solve this problem. Instead of applying the whole image as the input directly, we apply a square patch that contains only two superpixels as the input of the CNN. Also, instead of generating the segmentation result directly, the output of the CNN is whether the two superpixels should be merged. The proposed algorithm integrates the merits of conventional superpixel-based methods, feature-based methods, and CNN-based methods. Simulations show that the proposed algorithm can achieve very high accurate segmentation results and outperform state-of-the-art methods in all metrics. ? 2021 European Signal Processing Conference. All rights reserved. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123199311&doi=10.23919%2fEUSIPCO54536.2021.9616319&partnerID=40&md5=c484f2fa3503aafed66c5b7cef4d2390 https://scholars.lib.ntu.edu.tw/handle/123456789/607205 |
ISSN: | 22195491 | DOI: | 10.23919/EUSIPCO54536.2021.9616319 |
Appears in Collections: | 電機工程學系 |
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