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Green Image
Issue No. 11 - November (2008 vol. 30)
ISSN: 0162-8828
pp: 1877-1890
Yushi Jing , Georgia Institute of Technology, Atlanta, Google, Mountain View
Shumeet Baluja , Google, Mountain View
Because of the relative ease in understanding and processing text, commercial image-search systems often rely on techniques that are largely indistinguishable from text-search. Recently, academic studies have demonstrated the effectiveness of employing image-based features to provide alternative or additional signals. However, it remains uncertain whether such techniques will generalize to a large number of popular web queries, and whether the potential improvement to search quality warrants the additional computational cost. In this work, we cast the image-ranking problem into the task of identifying "authority" nodes on an inferred visual similarity graph and propose VisualRank to analyze the visual link structures among images. The images found to be "authorities" are chosen as those that answer the image-queries well. To understand the performance of such an approach in a real system, we conducted a series of large-scale experiments based on the task of retrieving images for 2000 of the most popular products queries. Our experimental results show significant improvement, in terms of user satisfaction and relevancy, in comparison to the most recent Google Image Search results. Maintaining modest computational cost is vital to ensuring that this procedure can be used in practice; we describe the techniques required to make this system practical for large scale deployment in commercial search engines.
Image Processing and Computer Vision, Image/video retrieval

Y. Jing and S. Baluja, "VisualRank: Applying PageRank to Large-Scale Image Search," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 1877-1890, 2008.
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