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Towards automated large scale discovery of image families

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4 Author(s)
Mohamed Aly ; Computational Vision Lab, Caltech, Pasadena, CA, USA ; Peter Welinder ; Mario Munich ; Pietro Perona

Gathering large collections of images is quite easy nowadays with the advent of image sharing Web sites, such as However, such collections inevitably 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. In this work, we investigate 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. We present our results on three datasets with different statistics, including two new challenging datasets. Moreover, we present a new algorithm to automatically determine the number of families in the collection with promising results.

Published in:

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Date of Conference:

20-25 June 2009