Paper Info
Title
Concept Detection in Multimedia Web Resources About Home Made Explosives
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.
Year
DOI
Venue
2015
10.1109/ARES.2015.85
International Conference on availability, reliability and security
Keywords
Field
DocType
concept detection, concept-based multimedia retrieval, visual feature extraction, home made explosives
Web resource,Data mining,Scale-invariant feature transform,Computer security,Segmentation,Computer science,Feature extraction,Law enforcement,Multimedia,Empirical research
Conference
Citations 
PageRank 
References 
1
0.37
28
Authors
8
Name
Order
Citations
PageRank
George Kalpakis132.78
Theodora Tsikrika232738.25
Fotini Markatopoulou3253.64
Nikiforos Pittaras4202.50
Stefanos Vrochidis526373.19
Vasileios Mezaris680381.40
Ioannis Patras71960123.15
Ioannis Kompatsiaris81404197.36