Summarization of Wireless Capsule Endoscopy Video Using Deep Feature Matching and Motion Analysis

Conventional Wireless capsule endoscopy (WCE) video summary generation techniques apprehend an image by extracting hand crafted features, which are not essentially sufficient to encapsulate the semantic similarity of endoscopic images. Use of supervised methods for extraction of deep features from a...

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Main Authors: B. Sushma, P. Aparna
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9293302/
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author B. Sushma
P. Aparna
author_facet B. Sushma
P. Aparna
author_sort B. Sushma
collection DOAJ
description Conventional Wireless capsule endoscopy (WCE) video summary generation techniques apprehend an image by extracting hand crafted features, which are not essentially sufficient to encapsulate the semantic similarity of endoscopic images. Use of supervised methods for extraction of deep features from an image need an enormous amount of accurate labelled data for training process. To solve this, we use an unsupervised learning method to extract features using convolutional auto encoder. Furthermore, WCE images are classified into similar and dissimilar pairs using fixed threshold derived through large number of experiments. Finally, keyframe extraction method based on motion analysis is used to derive a structured summary of WCE video. Proposed method achieves an average F-measure of 91.1% with compression ratio of 83.12%. The results indicate that the proposed method is more efficient compared to existing WCE video summarization techniques.
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spelling doaj.art-fcce0d83b06e400badc84b029a12ac6f2022-12-21T19:45:55ZengIEEEIEEE Access2169-35362021-01-019136911370310.1109/ACCESS.2020.30447599293302Summarization of Wireless Capsule Endoscopy Video Using Deep Feature Matching and Motion AnalysisB. Sushma0https://orcid.org/0000-0002-4581-1128P. Aparna1Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, IndiaDepartment of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, IndiaConventional Wireless capsule endoscopy (WCE) video summary generation techniques apprehend an image by extracting hand crafted features, which are not essentially sufficient to encapsulate the semantic similarity of endoscopic images. Use of supervised methods for extraction of deep features from an image need an enormous amount of accurate labelled data for training process. To solve this, we use an unsupervised learning method to extract features using convolutional auto encoder. Furthermore, WCE images are classified into similar and dissimilar pairs using fixed threshold derived through large number of experiments. Finally, keyframe extraction method based on motion analysis is used to derive a structured summary of WCE video. Proposed method achieves an average F-measure of 91.1% with compression ratio of 83.12%. The results indicate that the proposed method is more efficient compared to existing WCE video summarization techniques.https://ieeexplore.ieee.org/document/9293302/Autoencoderconvolutional neural networkdeep learningimage similaritykeyframe extractionvideo summarization
spellingShingle B. Sushma
P. Aparna
Summarization of Wireless Capsule Endoscopy Video Using Deep Feature Matching and Motion Analysis
IEEE Access
Autoencoder
convolutional neural network
deep learning
image similarity
keyframe extraction
video summarization
title Summarization of Wireless Capsule Endoscopy Video Using Deep Feature Matching and Motion Analysis
title_full Summarization of Wireless Capsule Endoscopy Video Using Deep Feature Matching and Motion Analysis
title_fullStr Summarization of Wireless Capsule Endoscopy Video Using Deep Feature Matching and Motion Analysis
title_full_unstemmed Summarization of Wireless Capsule Endoscopy Video Using Deep Feature Matching and Motion Analysis
title_short Summarization of Wireless Capsule Endoscopy Video Using Deep Feature Matching and Motion Analysis
title_sort summarization of wireless capsule endoscopy video using deep feature matching and motion analysis
topic Autoencoder
convolutional neural network
deep learning
image similarity
keyframe extraction
video summarization
url https://ieeexplore.ieee.org/document/9293302/
work_keys_str_mv AT bsushma summarizationofwirelesscapsuleendoscopyvideousingdeepfeaturematchingandmotionanalysis
AT paparna summarizationofwirelesscapsuleendoscopyvideousingdeepfeaturematchingandmotionanalysis