VisualRank: Applying PageRank to Large-Scale Image Search
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30 (2008), pp. 1877-1890
Publication Year
2008
Authors
Yushi Jing, Shumeet Baluja
BibTeX
Abstract
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 either alternative or additional signals
to use in this process. 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 2,000 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.
