Using deep features for video scene detection and annotation

The semantic video indexing problem is still underexplored. Solutions to the problem will significantly enrich the experience of video search, monitoring, and surveillance. This paper concerns scene detection and annotation, and specifically, the task of video structure mining for video indexing usi...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2018-07, Vol.12 (5), p.991-999
Main Authors: Protasov, Stanislav, Khan, Adil Mehmood, Sozykin, Konstantin, Ahmad, Muhammad
Format: Article
Language:English
Subjects:
Annotations
Artificial neural networks
Clustering
Computer Imaging
Computer Science
Feature extraction
Filtration
Image annotation
Image Processing and Computer Vision
Indexing
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Semantics
Signal,Image and Speech Processing
Vision
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Summary:The semantic video indexing problem is still underexplored. Solutions to the problem will significantly enrich the experience of video search, monitoring, and surveillance. This paper concerns scene detection and annotation, and specifically, the task of video structure mining for video indexing using deep features. The paper proposes and implements a pipeline that consists of feature extraction and filtering, shot clustering, and labeling stages. A deep convolutional network is used as the source of the features. The pipeline is evaluated using metrics for both scene detection and annotation. The results obtained show high scene detection and annotation quality estimated with various metrics. Additionally, we performed an overview and analysis of contemporary segmentation and annotation metrics. The outcome of this work can be applied to semantic video annotation in real time.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-018-1244-6