Content-Based Visual Summarization for Image Collections

Xingjia Pan, Fan Tang, Weiming Dong, Chongyang Ma, Yiping Meng, Feiyue Huang, Tong Yee Lee, Changsheng Xu

Research output: Contribution to journalArticlepeer-review

Abstract

With the surge of images in the information era, people demand an effective and accurate way to access meaningful visual information. Accordingly, effective and accurate communication of information has become indispensable. In this article, we propose a content-based approach that automatically generates a clear and informative visual summarization based on design principles and cognitive psychology to represent image collections. We first introduce a novel method to make representative and nonredundant summarizations of image collections, thereby ensuring data cleanliness and emphasizing important information. Then, we propose a tree-based algorithm with a two-step optimization strategy to generate the final layout that operates as follows: (1) an initial layout is created by constructing a tree randomly based on the grouping results of the input image set; (2) the layout is refined through a coarse adjustment in a greedy manner, followed by gradient back propagation drawing on the training procedure of neural networks. We demonstrate the usefulness and effectiveness of our method via extensive experimental results and user studies. Our visual summarization algorithm can precisely and efficiently capture the main content of image collections better than alternative methods or commercial tools.

Original languageEnglish
Article number8880504
Pages (from-to)2298-2312
Number of pages15
JournalIEEE Transactions on Visualization and Computer Graphics
Volume27
Issue number4
DOIs
Publication statusPublished - 2021 Apr 1

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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