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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Visual affective classification by combining visual and text features.

Ningning Liu1, Kai Wang2, Xin Jin3

  • 1School of Information technology and Management, University of International Business and Economics, BJ, P.R.China.

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Summary
This summary is machine-generated.

This study introduces a new method for visual affective classification (VAC) by fusing image features with text analysis. Combining visual and textual data significantly improves the accuracy of classifying emotions in images.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Affective Computing

Background:

  • Social media image analysis is crucial.
  • Textual context provides semantic meaning often missed by visual features alone.
  • Existing methods for visual affective classification (VAC) have limitations.

Purpose of the Study:

  • To propose a novel approach for visual affective classification (VAC).
  • To effectively combine visual representations with novel textual features for enhanced emotion detection.
  • To explore different fusion techniques for integrating visual and textual data.

Main Methods:

  • A fusion scheme based on Dempster-Shafer (D-S) Evidence Theory is proposed.
  • Investigated various visual features and fusion methods for VAC.
  • Developed novel textual features capturing emotional semantics from associated text using word similarity.

Main Results:

  • The proposed approach combining visual and textual features yields promising results for VAC.
  • Experiments conducted on IAPS, Artistic Photos, and MirFlickr Affect datasets validate the method.
  • The fusion of visual and textual information outperforms methods relying solely on visual data.

Conclusions:

  • The integration of visual and textual features offers a robust solution for visual affective classification.
  • The Dempster-Shafer Evidence Theory provides an effective framework for fusing multi-modal data in VAC.
  • This approach enhances the understanding of image content by leveraging associated textual semantics.