NDLI: MapReduce based content searching of surveillance system videos

Content Provider	IEEE Xplore Digital Library
Author	Zheng Xu Haiyan Chen
Copyright Year	2015
Description	Author affiliation: Tsinghua Univ., Beijing, China (Zheng Xu) \|\| East China Univ. of Political Sci. & Law, Shanghai, China (Haiyan Chen)
Abstract	In the last couple of decades, radio-frequency identification (RFID) technology has been widely used in logistics, manufacturing, defense, environment, health care, agriculture, retail, aviation, and information technology. CBIR systems go through sets of stages starting from acquiring the new images, representing these images by extracting the image features, describing the key features and eventually computing the similarity distances to get the most relevant results responding to the query image. In this paper, an integrated CBIR Hadoop-MapReduce based framework which is split into both offline and online phases is introduced. Visual statements are built using the extracted interest points SIFTs. Later on, these visual statements are used to estimate the similarity distances which in turn are used to create the image dataset clusters. A huge vocabulary of SIFTs describing the interest points of the image is constructed. Corresponding statements which reflect the visual content for these features are created by applying the HAC technique.
Starting Page	250
Ending Page	254
File Size	120482
Page Count	5
File Format	PDF
e-ISBN	9781467372909
DOI	10.1109/ICCI-CC.2015.7259393
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Logistics Analytical models Radiofrequency identification Videos Fuels Jacobian matrices CBIR MapReduce Surveillance System
Content Type	Text
Resource Type	Article

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