NDLI: A Hamming Embedding Kernel with Informative Bag-of-Visual Words for Video Semantic Indexing (TOMM, Vol.10, Issue 3)

Content Provider	ACM Digital Library
Author	Ngo, Chong-Wah Merialdo, Bernard Zhao, Wan-Lei Wang, Feng
Copyright Year	2014
Abstract	In this article, we propose a novel Hamming embedding kernel with informative bag-of-visual words to address two main problems existing in traditional BoW approaches for video semantic indexing. First, Hamming embedding is employed to alleviate the information loss caused by SIFT quantization. The Hamming distances between keypoints in the same cell are calculated and integrated into the SVM kernel to better discriminate different image samples. Second, to highlight the concept-specific visual information, we propose to weight the visual words according to their informativeness for detecting specific concepts. We show that our proposed kernels can significantly improve the performance of concept detection.
Starting Page	1
Ending Page	20
Page Count	20
File Format	PDF
ISSN	15516857
e-ISSN	15516865
DOI	10.1145/2535938
Volume Number	10
Issue Number	3
Journal	ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-04-17
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bag-of-visual word Hamming embedding Kernel optimization Video semantic indexing
Content Type	Text
Resource Type	Article
Subject	Hardware and Architecture Computer Networks and Communications

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A Hamming Embedding Kernel with Informative Bag-of-Visual Words for Video Semantic Indexing (TOMM, Vol.10, Issue 3)