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Sparse coded handcrafted and deep features for colon capsule video summarization

Mohammed, Ahmed Kedir; Yildirim, Sule; Pedersen, Marius; Hovde, Øistein; Alaya Cheikh, Faouzi
Journal article
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URI
http://hdl.handle.net/11250/2492491
Date
2017
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  • Institutt for datateknologi og informatikk [4938]
  • Institutt for informasjonssikkerhet og kommunikasjonsteknologi [1955]
  • Publikasjoner fra CRIStin - NTNU [26717]
Original version
Computer-Based Medical Systems. 2017, 2017-June 728-733.   10.1109/CBMS.2017.13
Abstract
Capsule endoscopy, which uses a wireless camera to take images of the digestive track, is emerging as an alternative to traditional wired colonoscopy. A single examination produces a sequence of approximately 50,000 frames. These sequences are manually reviewed, which is time consuming and typically takes about 45-90 minutes and requires the undivided concentration of the reviewer. In this paper, we propose a novel capsule video summarization framework using sparse coding and dictionary learning in feature space. Video frames are clustered into superframes based on power spectral density, and cluster representative frames are used for video summarization. Handcrafted and deep features that are extracted for representative frames are sparse coded using a learned dictionary. Sparse coded features are later used for training SVM classifier. The proposed method was compared with state-of-the-art methods based on sensitivity and specificity. The achieved results show that our proposed framework provides robust capsule video summarization without losing informative segments.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Journal
Computer-Based Medical Systems

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