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Image Clustering using Color, Texture and Shape Features
( Azzam Sleit ),( Abdel Latif Abu Dalhoum ),( Mohammad Qatawneh ),( Maryam Al-sharief ),( Rawa`a Al-jabaly ),( Ola Karajeh ) 한국인터넷정보학회 2011 KSII Transactions on Internet and Information Syst Vol.5 No.1
Content Based Image Retrieval (CBIR) is an approach for retrieving similar images from an image database based on automatically-derived image features. The quality of a retrieval system depends on the features used to describe image content. In this paper, we propose an image clustering system that takes a database of images as input and clusters them using k-means clustering algorithm taking into consideration color, texture and shape features. Experimental results show that the combination of the three features brings about higher values of accuracy and precision.
( Asma Salem ),( Ahmad Sharieh ),( Azzam Sleit ),( Riad Jabri ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.8
Nowadays, most users access internet through mobile applications. The common way to authenticate users through websites forms is using passwords; while they are efficient procedures, they are subject to guessed or forgotten and many other problems. Additional multi modal authentication procedures are needed to improve the security. Behavioral authentication is a way to authenticate people based on their typing behavior. It is used as a second factor authentication technique beside the passwords that will strength the authentication effectively. Keystroke dynamic rhythm is one of these behavioral authentication methods. Keystroke dynamics relies on a combination of features that are extracted and processed from typing behavior of users on the touched screen and smart mobile users. This Research presents a novel analysis in the keystroke dynamic authentication field using two features categories: timing and no timing combined features. The proposed model achieved lower error rate of false acceptance rate with 0.1%, false rejection rate with 0.8%, and equal error rate with 0.45%. A comparison in the performance measures is also given for multiple datasets collected in purpose to this research.