Khan, M.U.G. and Gotoh, Y. (2017) Generating natural language tags for video information management. Machine Vision and Applications, 28 (3-4). pp. 243-265. ISSN 0932-8092
Abstract
This exploratory work is concerned with generation of natural language descriptions that can be used for video retrieval applications. It is a step ahead of keyword-based tagging as it captures relations between keywords associated with videos. Firstly, we prepare hand annotations consisting of descriptions for video segments crafted from a TREC Video dataset. Analysis of this data presents insights into human’s interests on video contents. Secondly, we develop a framework for creating smooth and coherent description of video streams. It builds on conventional image processing techniques that extract high-level features from individual video frames. Natural language description is then produced based on high-level features. Although feature extraction processes are erroneous at various levels, we explore approaches to putting them together to produce a coherent, smooth and well-phrased description by incorporating spatial and temporal information. Evaluation is made by calculating ROUGE scores between human-annotated and machine-generated descriptions. Further, we introduce a task-based evaluation by human subjects which provides qualitative evaluation of generated descriptions.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Springer. This is an author produced version of a paper subsequently published in Machine Vision and Applications . Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Video information management; Video annotation; Natural language generation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Feb 2017 11:21 |
Last Modified: | 07 Jul 2023 15:58 |
Status: | Published |
Publisher: | Springer Verlag (Germany) |
Refereed: | Yes |
Identification Number: | 10.1007/s00138-017-0825-7 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:112427 |