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Embedded intention scripts representation and real-time interpretation metrics extraction methodology with gaze annotation on visual content

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Abstract

This paper proposes a system platform with a capability of analyzing and measuring the degree of similarities and differences of the gaze behaviors of the multiple users with the intention of the provider in real-time. In the proposed system platform, a video content incorporates the intention flow script, which describes the intended sequence and duration of the information embedded into the visual content. The video content also incorporates the intention weight script for assigning importance values to individual object information so that the significance of the gaze behavior can be assesses. In this paper, a set of evaluation metrics and their usages are defined, which can be used to evaluate the degree of gazing behavior closeness and deviation from the intention. The proposed method can be applied in many applications, such as in educational and commercial domains, where the information contents are usually designed with a specific intent. In the paper, we verified the proposed method by incorporation intention script into a sequence of educational image frames to analyze the gaze behavior of multiple users.

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Acknowledgments

This research was supported by the ‘Cross-Ministry Giga KOREA Project’ of the Ministry of Science, ICT and Future Planning, Republic of Korea (ROK) [GK15P0100, Development of Tele-Experience Service SW Platform based on Giga Media].

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, New York, NY, 11794, USA

    Jung-Min Oh & Sangjin Hong

  2. Department of Computer Engineering, Hoseo University, Asan, 336-795, Korea

    Nammee Moon

Authors
  1. Jung-Min Oh
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  2. Sangjin Hong
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  3. Nammee Moon
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Correspondence to Nammee Moon.

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Oh, JM., Hong, S. & Moon, N. Embedded intention scripts representation and real-time interpretation metrics extraction methodology with gaze annotation on visual content. Multimed Tools Appl 75, 7271–7291 (2016). https://doi.org/10.1007/s11042-015-2644-z

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  • DOI: https://doi.org/10.1007/s11042-015-2644-z

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