Automated event clustering and quality screening of consumer pictures for digital albuming

In this paper, algorithms for automatic albuming of consumer photographs are described. Specifically, two core algorithms namely event clustering and screening of low-quality images, are introduced and their performance is evaluated. Event clustering and image quality screening have many applications including albuming services, image management and organization, and digital photofinishing. These are difficult tasks because there is, in general, none (or very limited) contextual information about picture content, and the final interpretation could be subjective. A novel event-clustering algorithm is created to automatically segment pictures into events and subevents for albuming, based on date/time metadata information, as well as color content of the pictures. A block-based color histogram correlation technique is developed for image content comparison of general consumer pictures. A new quality-screening algorithm is developed based on object quality measures, to detect problematic images caused by underexposure, low contrast, and camera defocus or movement. Performance testing of these algorithms was conducted using a database of real consumer photos and showed that these functions provide a useful first-cut album layout for typical rolls of consumer pictures. The proposed techniques and system represent a major step toward automatic albuming of consumer pictures. Automatic albuming application software which, based on the above image event clustering and quality screening algorithms, has been successfully tested and validated through a recent consumer trial in the United States.