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Authors: Kazuya Ueki and Tetsunori Kobayashi

Affiliation: Waseda University, Japan

Keyword(s): Video Semantic Indexing, Video Retrieval, Object Detection, Convolutional Neural Network.

Abstract: We propose a new feature extraction method for video semantic indexing. Conventional methods extract features densely and uniformly across an entire image, whereas the proposed method exploits the object detector to extract features from image windows with high objectness. This feature extraction method focuses on ``objects.'' Therefore, we can eliminate the unnecessary background information, and keep the useful information such as the position, the size, and the aspect ratio of a object. Since these object detection oriented features are complementary to features from entire images, the performance of video semantic indexing can be further improved. Experimental comparisons using large-scale video dataset of the TRECVID benchmark demonstrated that the proposed method substantially improved the performance of video semantic indexing.

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Paper citation in several formats:
Ueki, K. and Kobayashi, T. (2017). Object Detection Oriented Feature Pooling for Video Semantic Indexing. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP; ISBN 978-989-758-226-4; ISSN 2184-4321, SciTePress, pages 44-51. DOI: 10.5220/0006099600440051

@conference{visapp17,
author={Kazuya Ueki. and Tetsunori Kobayashi.},
title={Object Detection Oriented Feature Pooling for Video Semantic Indexing},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP},
year={2017},
pages={44-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006099600440051},
isbn={978-989-758-226-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP
TI - Object Detection Oriented Feature Pooling for Video Semantic Indexing
SN - 978-989-758-226-4
IS - 2184-4321
AU - Ueki, K.
AU - Kobayashi, T.
PY - 2017
SP - 44
EP - 51
DO - 10.5220/0006099600440051
PB - SciTePress

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