A STUDY OF X-RAY MACHINE IMAGE LOCAL SEMANTIC FEATURES EXTRACTION MODEL BASED ON BAG-OFWORDS FOR AIRPORT SECURITY

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

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VOLUME 8 , ISSUE 1 (March 2015) > List of articles

A STUDY OF X-RAY MACHINE IMAGE LOCAL SEMANTIC FEATURES EXTRACTION MODEL BASED ON BAG-OFWORDS FOR AIRPORT SECURITY

Ning Zhang / Jinfu Zhu

Keywords : airport security, bag-of-words, semantic analysis, image features

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 1, Pages 45-64, DOI: https://doi.org/10.21307/ijssis-2017-748

License : (CC BY-NC-ND 4.0)

Received Date : 25-August-2014 / Accepted: 07-January-2015 / Published Online: 01-March-2015

ARTICLE

ABSTRACT

The aviation security at the airport has been faced with increasingly severe situations since the 9-11 event. It’s of utmost importance to train airport X-ray machine screener’s image recognition competency. So they can prevent terrorists from bringing dangerous articles in their carry-on or checked bags. However, usually the luggages are placed in different positions and the density & volume of articles differ greatly. As a result, dangerous articles show a variety of X-ray image features. It’s easy for the confused screeners to miss or incorrectly detect dangerous articles. This has been a hidden danger for civil aviation safety. For image recognition improvement, the researcher analyzed the visual semantics of dangerous goods images and applied a local semantic features extraction method. After classification and summarization, the method was used to train the screeners for particular image recognition. The comparison showed the improved accuracy and efficiency of image recognition for the screeners and demonstrated a satisfactory effect.

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