Automatic name-face alignment to enable cross-media news retrieval

  • Authors:

  • Yuejie Zhang;Wei Wu;Yang Li;Cheng Jin;Xiangyang Xue;Jianping Fan

  • Affiliations:

  • School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China;School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China;School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China;School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China;School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China;Department of Computer Science, The University of North Carolina at Charlotte

  • Venue:
  • IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

A new algorithm is developed in this paper to support automatic name-face alignment for achieving more accurate cross-media news retrieval. We focus on extracting valuable information from large amounts of news images and their captions, where multi-level image-caption pairs are constructed for characterizing both significant names with higher salience and their cohesion with human faces extracted from news images. To remedy the issue of lacking enough related information for rare name, Web mining is introduced to acquire the extra multimodal information. We also emphasize on an optimization mechanism by our Improved Self-Adaptive Simulated Annealing Genetic Algorithm to verify the feasibility of alignment combinations. Our experiments have obtained very positive results.