NDLI: A new gaze points agglomerative clustering algorithm and its application in regions of interest extraction

Content Provider	IEEE Xplore Digital Library
Author	Al-Azawi, M. Yang, Y. Istance, H.
Copyright Year	2014
Description	Author affiliation: Centre for Comput. Intell., De Montfort Univ., Leicester, UK (Al-Azawi, M.; Yang, Y.; Istance, H.)
Abstract	In computer vision applications it is necessary to extract the regions of interest in order to reduce the search space and to improve image contents identification. Human-Oriented Regions of Interest can be extracted by collecting some feedback from the user. The feedback usually provided by the user by giving different ranks for the identified regions in the image. This rank is then used to adapt the identification process. Nowadays eye tracking technology is widely used in different applications, one of the suggested applications is by using the data collected from the eye-tracking device, which represents the user gaze points in extracting the regions of interest. In this paper we shall introduce a new agglomerative clustering algorithm which uses blobs extraction technique and statistical measures in clustering the gaze points obtained from the eye tracker. The algorithm is fully automatic, which means does not need any human intervention to specify the stopping criterion. In the suggested algorithm the points are replaced with small regions (blobs) then these blobs are grouped together to form a cloud, from which the interesting regions are constructed.
Sponsorship	IEEE India Counc. C Chapt.
Starting Page	946
Ending Page	951
File Size	505776
Page Count	6
File Format	PDF
e-ISBN	9781479925728
DOI	10.1109/IAdCC.2014.6779450
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-02-21
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision image Shape Human Attention Conferences Clustering algorithms Gaze points Classification algorithms Data mining Mathematical model Clustering Standards
Content Type	Text
Resource Type	Article

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