Summarizing visual data using bidirectional similarity

We propose a principled approach to summarization of visual data (images or video) based on optimization of a well-defined similarity measure. The problem we consider is re-targeting (or summarization) of image/video data into smaller sizes. A good "visual summary" should satisfy two properties: (1) it should contain as much as possible visual information from the input data; (2) it should introduce as few as possible new visual artifacts that were not in the input data (i.e., preserve visual coherence). We propose a bi-directional similarity measure which quantitatively captures these two requirements: Two signals S and T are considered visually similar if all patches of S (at multiple scales) are contained in T, and vice versa. The problem of summarization/re-targeting is posed as an optimization problem of this bi-directional similarity measure. We show summarization results for image and video data. We further show that the same approach can be used to address a variety ...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Denis Simakov, Yaron Caspi, Eli Shechtman, Michal Irani
• Keywords: Bi-directional Similarity Measure, Computer Vision, CVPR 2008, Possible Visual Information, Preserve Visual Coherence, Visual Data, Well-defined Similarity Measure