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DEL: Deep embedding learning for efficient image segmentation
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
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2018 (English)In: IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence , 2018, p. 864-870Conference paper, Published paper (Refereed)
Abstract [en]

Image segmentation has been explored for many years and still remains a crucial vision problem. Some efficient or accurate segmentation algorithms have been widely used in many vision applications. However, it is difficult to design a both efficient and accurate image segmenter. In this paper, we propose a novel method called DEL (deep embedding learning) which can efficiently transform superpixels into image segmentation. Starting with the SLIC superpixels, we train a fully convolutional network to learn the feature embedding space for each superpixel. The learned feature embedding corresponds to a similarity measure that measures the similarity between two adjacent superpixels. With the deep similarities, we can directly merge the superpixels into large segments. The evaluation results on BSDS500 and PASCAL Context demonstrate that our approach achieves a good tradeoff between efficiency and effectiveness. Specifically, our DEL algorithm can achieve comparable segments when compared with MCG but is much faster than it, i.e. 11.4fps vs. 0.07fps. 

Place, publisher, year, edition, pages
International Joint Conferences on Artificial Intelligence , 2018. p. 864-870
Keywords [en]
Artificial intelligence, Deep learning, Pixels, Superpixels, Convolutional networks, Evaluation results, Feature embedding, Segmentation algorithms, Segmenter, Similarity measure, Vision applications, Vision problems, Image segmentation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-246576Scopus ID: 2-s2.0-85054182458OAI: oai:DiVA.org:kth-246576DiVA, id: diva2:1322779
Conference
27th International Joint Conference on Artificial Intelligence, IJCAI 2018, 13 July 2018 through 19 July 2018
Note

QC 20190611

Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-06-11Bibliographically approved

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