Automatic thumbnail cropping and its effectiveness

  • Authors:

  • Bongwon Suh;Haibin Ling;Benjamin B. Bederson;David W. Jacobs

  • Affiliations:

  • Human-Computer Interaction Laboratory, University of Maryland, College Park, MD;Department of Computer Science, University of Maryland, College Park, MD;Human-Computer Interaction Laboratory, University of Maryland, College Park, MD;Department of Computer Science, University of Maryland, College Park, MD

  • Venue:
  • Proceedings of the 16th annual ACM symposium on User interface software and technology
  • Year:
  • 2003

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Abstract

Thumbnail images provide users of image retrieval and browsing systems with a method for quickly scanning large numbers of images. Recognizing the objects in an image is important in many retrieval tasks, but thumbnails generated by shrinking the original image often render objects illegible. We study the ability of computer vision systems to detect key components of images so that automated cropping, prior to shrinking, can render objects more recognizable. We evaluate automatic cropping techniques 1) based on a general method that detects salient portions of images, and 2) based on automatic face detection. Our user study shows that these methods result in small thumbnails that are substantially more recognizable and easier to find in the context of visual search.