Paper Info
Title
Scalable gastroscopic video summarization via similar-inhibition dictionary selection.
Abstract
HighlightsWe design a dictionary selection model via the similar-inhibition constraint.We propose a scalable gastroscopic video summarization algorithm.We build the first gastroscopic video summarization dataset with 30 videos. ObjectiveThis paper aims at developing an automated gastroscopic video summarization algorithm to assist clinicians to more effectively go through the abnormal contents of the video. Methods and materialsTo select the most representative frames from the original video sequence, we formulate the problem of gastroscopic video summarization as a dictionary selection issue. Different from the traditional dictionary selection methods, which take into account only the number and reconstruction ability of selected key frames, our model introduces the similar-inhibition constraint to reinforce the diversity of selected key frames. We calculate the attention cost by merging both gaze and content change into a prior cue to help select the frames with more high-level semantic information. Moreover, we adopt an image quality evaluation process to eliminate the interference of the poor quality images and a segmentation process to reduce the computational complexity. ResultsFor experiments, we build a new gastroscopic video dataset captured from 30 volunteers with more than 400k images and compare our method with the state-of-the-arts using the content consistency, index consistency and content-index consistency with the ground truth. Compared with all competitors, our method obtains the best results in 23 of 30 videos evaluated based on content consistency, 24 of 30 videos evaluated based on index consistency and all videos evaluated based on content-index consistency. ConclusionsFor gastroscopic video summarization, we propose an automated annotation method via similar-inhibition dictionary selection. Our model can achieve better performance compared with other state-of-the-art models and supplies more suitable key frames for diagnosis. The developed algorithm can be automatically adapted to various real applications, such as the training of young clinicians, computer-aided diagnosis or medical report generation.
Year
DOI
Venue
2016
10.1016/j.artmed.2015.08.006
Artificial Intelligence in Medicine
Keywords
Field
DocType
Video summarization,Key frame,Similar-inhibition dictionary selection,Image attention prior,Gastroscopic video
Data mining,Computer science,Image quality,Artificial intelligence,Key frame,Automatic summarization,Information retrieval,Segmentation,Ground truth,Data compression,Machine learning,Scalability,Computational complexity theory
Journal
Volume
Issue
ISSN
66
C
0933-3657
Citations 
PageRank 
References 
5
0.40
48
Authors
7
Name
Order
Citations
PageRank
Shuai Wang1242.98
Yang Cong268438.22
Jun Cao3231.05
Yunsheng Yang4203.95
Y. Tang524333.69
Huaici Zhao6144.91
Haibin Yu720125.62