![]() DescriptionGathering large collections of images is quite easy nowadays with the advent of image sharing websites. However, such collections contain duplicates and highly similar images, what we refer to as image families. Automatic discovery and cataloguing of such similar images in large collections is important for many applications, e.g. image search, image collection visualization, and research purposes among others.This work investigates this problem by thoroughly comparing two broad approaches for measuring image similarity: global vs. local features. We assess their performance as the image collection scales up to over 11,000 images with over 6,300 families. Moreover, we present a new algorithm to automatically determine the number of families in the collection. References
AcknowledgementsThis is a joint work with Peter Welinder, Mario Munich, and Pietro Perona. |
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