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Mining from Large Image Sets

Luc Van Gool, Michael D. Breitenstein, Stephan Gammeter, Helmut Grabner, Till Quack
ACM Int. Conference on Image and Video Retrieval
July 2009


So far, most image mining was based on interactive querying. Although such querying will remain important in the future, several applications need image mining at such wide scales that it has to run automatically. This adds an additional level to the problem, namely to apply appropriate further processing to different types of images, and to decide on such processing automatically as well. This paper touches on those issues in that we discuss the processing of landmark images and of images coming from webcams. The first part deals with the automated collection of images of landmarks, which are then also automatically annotated and enriched with Wikipedia information. The target application is that users photograph landmarks with their mobile phones or PDAs, and automatically get information about them. Similarly, users can get images in their photo albums annotated automatically. The object of interest can also be automatically delineated in the images. The pipeline we propose actually retrieves more images than manual keyword input would produce. The second part of the paper deals with an entirely different source of image data, but one that also produces massive amounts (although typically not archived): webcams. They produce images at a single location, but rather continuously and over extended periods of time. We propose an approach to summarize data coming from webcams. This data handling is quite different from that applied to the landmark images.

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  author = {Luc Van Gool and Michael D. Breitenstein and Stephan Gammeter and Helmut Grabner and Till Quack},
  title = {Mining from Large Image Sets},
  booktitle = {ACM Int. Conference on Image and Video Retrieval},
  year = {2009},
  month = {July},
  keywords = {}