Video Shots‘ Matching via Various Length of Multidimensional Time Sequences | Zendy

Zhengbing Hu | Zendy; Sergii Mashtalir | Zendy; Oleksii K. Tyshchenko | Zendy; Mykhailo Stolbovyi | Zendy

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Video Shots‘ Matching via Various Length of Multidimensional Time Sequences

Author(s) -

Zhengbing Hu,

Sergii Mashtalir,

Oleksii K. Tyshchenko,

Mykhailo Stolbovyi

Publication year - 2017

Publication title -

international journal of intelligent systems and applications

Language(s) - English

Resource type - Journals

eISSN - 2074-9058

pISSN - 2074-904X

DOI - 10.5815/ijisa.2017.11.02

Subject(s) - dynamic time warping , computer science , cluster analysis , segmentation , image warping , similarity (geometry) , series (stratigraphy) , matching (statistics) , artificial intelligence , structuring , computer vision , pattern recognition (psychology) , image (mathematics) , statistics , mathematics , paleontology , finance , economics , biology

Temporal clustering (segmentation) for video streams has revolutionized the world of multimedia. Detected shots are principle units of consecutive sets of images for semantic structuring. Evaluation of time series similarity is based on Dynamic Time Warping and provides various solutions for Content Based Video Information Retrieval. Time series clustering in terms of the iterative Dynamic Time Warping and time series reduction are discussed in the paper.

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