Algorithms for clustering data
Algorithms for clustering data
The Johnson-Lindenstrauss Lemma and the sphericity of some graphs
Journal of Combinatorial Theory Series A
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Gazetracker: software designed to facilitate eye movement analysis
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
Identifying fixations and saccades in eye-tracking protocols
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
EDBT '02 Proceedings of the Worshops XMLDM, MDDE, and YRWS on XML-Based Data Management and Multimedia Engineering-Revised Papers
Multimedia Mining: A Highway to Intelligent Multimedia Documents (Multimedia Systems and Applications Series)
Robust clustering of eye movement recordings for quantification of visual interest
Proceedings of the 2004 symposium on Eye tracking research & applications
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Eye/gaze tracking in web, image and video documents
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Where people look when watching movies: Do all viewers look at the same place?
Computers in Biology and Medicine
Stars in their eyes: what eye-tracking reveals about multimedia perceptual quality
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Analyzing eye fixations and gaze orientations on films and pictures
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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Eye movements are certainly the most natural and repetitive movement of a human being. The most mundane activity, such as watching television or reading a newspaper, involves this automatic activity which consists of shifting our gaze from one point to another. Identification of the components of eye movements (fixations and saccades) is an essential part in the analysis of visual behavior because these types of movements provide the basic elements used by further investigations of human vision. However, many of the algorithms that detect fixations present a number of problems. In this article, we present a new fixation identification technique that is based on clustering of eye positions, using projections and projection aggregation applied to static pictures. We also present a new method that computes dispersion of eye fixations in videos considering a multiuser environment. To demonstrate the performance and usefulness of our approach we discuss our experimental work with two different applications: on fixed image and video.