NDLI: Vectors of locally aggregated centers for compact video representation

Content Provider	IEEE Xplore Digital Library
Author	Abbas, A. Deligiannis, N. Andreopoulos, Y.
Copyright Year	2015
Description	Author affiliation: Dept. of Electron. & Electr. Eng., Univ. Coll. London (UCL), London, UK (Abbas, A.; Deligiannis, N.; Andreopoulos, Y.)
Abstract	We propose a novel vector aggregation technique for compact video representation, with application in accurate similarity detection within large video datasets. The current state-of-the-art in visual search is formed by the vector of locally aggregated descriptors (VLAD) of Jegou et al. VLAD generates compact video representations based on scale-invariant feature transform (SIFT) vectors (extracted per frame) and local feature centers computed over a training set. With the aim to increase robustness to visual distortions, we propose a new approach that operates at a coarser level in the feature representation. We create vectors of locally aggregated centers (VLAC) by first clustering SIFT features to obtain local feature centers (LFCs) and then encoding the latter with respect to given centers of local feature centers (CLFCs), extracted from a training set. The sum-of-differences between the LFCs and the CLFCs are aggregated to generate an extremely-compact video description used for accurate video segment similarity detection. Experimentation using a video dataset, comprising more than 1000 minutes of content from the Open Video Project, shows that VLAC obtains substantial gains in terms of mean Average Precision (mAP) against VLAD and the hyper-pooling method of Douze et al., under the same compaction factor and the same set of distortions.
Starting Page	1
Ending Page	6
File Size	127177
Page Count	6
File Format	PDF
ISBN	9781479970827
DOI	10.1109/ICME.2015.7177501
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-29
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Distortion Training Feature extraction Visualization Compaction Principal component analysis Robustness scale-invariant feature transform video similarity vector of locally aggregated descriptors
Content Type	Text
Resource Type	Article

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