Large-Scale Endoscopic Image and Video Linking with Gradient-Based Signatures (bibtex)
@InProceedings{Beecks2017, title = {Large-Scale Endoscopic Image and Video Linking with Gradient-Based Signatures}, author = {Beecks, Christian and Kletz, Sabrina and Schoeffmann, Klaus}, booktitle = {Proceedings of the Third IEEE International Conference on Multimedia Big Data (BigMM 2017)}, year = {2017}, address = {Laguna Hills, California, USA}, editor = {Chen, Shu-Ching and Sheu, Philip Chen-Yu}, month = {apr}, pages = {5}, publisher = {IEEE}, series = {BigMM}, abstract = {Given a large-scale video archive of surgical interventions and a medical image showing a specific moment of an operation, how to find the most image-related videos efficiently without the utilization of additional semantic characteristics? In this paper, we investigate a novel content-based approach of linking medical images with relevant video segments arising from endoscopic procedures. We propose to approximate the video segments' content-based features by gradient-based signatures and to index these signatures with the Minkowski distance in order to determine the most query-like video segments efficiently. We benchmark our approach on a large endoscopic image and video archive and show that our approach achieves a significant improvement in efficiency in comparison to the state-of-the-art while maintaining high accuracy.}, doi = {10.1109/BigMM.2017.44}, keywords = {feature signatures, laparoscopic video, medical endoscopy, motion analysis, similarity search, video retrieval}, language = {EN}, location = {Laguna Hills, California, USA}, talkdate = {2017.04.19}, talktype = {registered}, url = {} }
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