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Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Imprimir: 1064-2315
ISSN On-line: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v51.i3.30
pages 26-35

Hybrid Approach to Clustering Various Lengths Video

Sergey V. Mashtalir
Kharkov National University of Radio and Electronics, Kharkov
Mikhail І. Stolbovoi
Kharkov National University of Radio and Electronics, Kharkov
Sergey V. Yakovlev
N.E. Zhukovskiy National Aerospace University "Kharkov Aviation Institute", Kharkov


A significant increase in the amount of data to be analyzed and processed requires the introduction of new efficient tools and methods for their collection and storage. This task is especially important when analyzing multimedia in particular video data due to their great redundancy. One of the ways to reduce the amount of information processed is clustering/segmentation of video sequences to isolate parts that are homogeneous in content. This raises the problem of choosing the required number of clusters as an information source. The article is devoted to the development of a hybrid clustering method for analyzing video sequences of various lengths. The method saves the advantages and eliminates the disadvantages of agglomerative hierarchical and fuzzy clusterings. To determine the similarity between segments of video sequences, the Levenshtein metric is used, which allows one to calculate the distances between multidimensional sequences of different lengths. The criterion for the clustering process completion as a whole, and, accordingly, the result quality is determined by the Dunn index. The proposed hybrid approach to clustering video sequences is computationally simple to implement and allows solving the multidimensional time series analysis problems of arbitrary nature in the case when it is difficult to determine in advance the necessary number of clusters for splitting and under conditions of uncertainty about their possible overlap, i.e. in the case where the clustering result is the cover construction, and not data partitioning (exact cover construction).


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