Finding an interesting frame in a video

Question

Does anyone know of an algorithm that I could use to find an \"interesting\" representative thumbnail for a video?

I have say 30 bitmaps and I would like to choose the

Answer 1

You asked for papers so I found a few. If you are not on campus or on VPN connection to campus these papers might be hard to reach.

PanoramaExcerpts: extracting and packing panoramas for video browsing

http://portal.acm.org/citation.cfm?id=266396

This one explains a method for generating a comicbook style keyframe representation.

Abstract:

This paper presents methods for automatically creating pictorial video summaries that resem- ble comic books. The relative importance of video segments is computed from their length and novelty. Image and audio analysis is used to automatically detect and emphasize mean- ingful events. Based on this importance mea- sure, we choose relevant keyframes. Selected keyframes are sized by importance, and then efficiently packed into a pictorial summary. We present a quantitative measure of how well a summary captures the salient events in a video, and show how it can be used to improve our summaries. The result is a compact and visually pleasing summary that captures semantically important events, and is suitable for printing or Web access. Such a summary can be further enhanced by including text cap- tions derived from OCR or other methods. We describe how the automatically generated sum- maries are used to simplify access to a large collection of videos.

Automatic extraction of representative keyframes based on scenecontent

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=751008

Abstract:

Generating indices for movies is a tedious and expensive process which we seek to automate. While algorithms for finding scene boundaries are readily available, there has been little work performed on selecting individual frames to concisely represent the scene. In this paper we present novel algorithms for automated selection of representative keyframes, based on scene content. Detailed description of several algorithms is followed by an analysis of how well humans feel the selected frames represent the scene. Finally we address how these algorithms can be integrated with existing algorithms for finding scene boundaries.