A system for automatically summarizing your digital photographs to make them easier to find.


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About AutoAlbum & PhotoTOC

It is getting increasingly popular for consumers to buy a digital camera and take thousands of photos of daily life. Most consumers simply dump these photos into one directory, analogous to dumping developed prints into a shoebox. A typical user generates thousands of photos a year. Finding a photo in this shoebox directory is difficult. AutoAlbum and PhotoTOC are browsing user interfaces that help solve this problem. AutoAlbum was the original UI, while PhotoTOC is a new, updated UI. PhotoTOC consists of two panes. Thumbnails of all images in the shoebox directory is shown on the right pane, as a big contact sheet. PhotoTOC automatically clusters these images. One representative photograph from every cluster is shown on the left pane. When the user clicks on a representative photograph, the right pane scrolls to show that same photograph in the center of the window. The user can then find his/her photograph with minimal scrolling on the right-hand pane. Both AutoAlbum and PhotoTOC use two forms of metadata to help cluster the photos: the creation time of the photo and the order that the photos were taken. Under some circumstances, the creation time of the photo is preserved after download from the camera to the PC. In those cases, PhotoTOC can cluster on the creation time and ignore the content. In other cases, the creation time is destroyed (e.g., by downloading with a serial cable, or camera running out of batteries). However, the order of the photographs is still often preserved, via either download time or file name. In this second case, PhotoTOC uses the content of the photos to cluster, but creates clusters that obey the photographic order. In the demo, the combination of these two clustering techniques are used: first, time-based clustering uses the creation date of the file to form albums. If the creation date of the file is not the creation date of the photo, time-based clustering will produce very large clusters. So, any very large cluster will get broken down by content-based clustering that obeys the order of the photographs.

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