NDLI: Concept Detection in Multimedia Web Resources About Home Made Explosives

Content Provider	IEEE Xplore Digital Library
Author	Kalpakis, G. Tsikrika, T. Markatopoulou, F. Pittaras, N. Vrochidis, S. Mezaris, V. Patras, I. Kompatsiaris, I.
Copyright Year	2015
Description	Author affiliation: Inf. Technol. Inst., CERTH, Thessaloniki, Greece (Kalpakis, G.; Tsikrika, T.; Markatopoulou, F.; Pittaras, N.; Vrochidis, S.; Mezaris, V.; Kompatsiaris, I.) \|\| Queen Mary Univ. of London, London, UK (Patras, I.)
Abstract	This work investigates the effectiveness of a state-of-the-art concept detection framework for the automatic classification of multimedia content, namely images and videos, embedded in publicly available Web resources containing recipes for the synthesis of Home Made Explosives (HMEs), to a set of predefined semantic concepts relevant to the HME domain. The concept detection framework employs advanced methods for video (shot) segmentation, visual feature extraction (using SIFT, SURF, and their variations), and classification based on machine learning techniques (logistic regression). The evaluation experiments are performed using an annotated collection of multimedia HME content discovered on the Web, and a set of concepts, which emerged both from an empirical study, and were also provided by domain experts and interested stakeholders, including Law Enforcement Agencies personnel. The experiments demonstrate the satisfactory performance of our framework, which in turn indicates the significant potential of the adopted approaches on the HME domain.
Starting Page	632
Ending Page	641
File Size	905735
Page Count	10
File Format	PDF
e-ISBN	9781467365901
DOI	10.1109/ARES.2015.85
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-24
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	concept detection Visualization Semantics Streaming media home made explosives visual feature extraction Feature extraction Multimedia communication Videos concept-based multimedia retrieval
Content Type	Text
Resource Type	Article

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