PageRank for Product Image Search
In this paper, we cast the image-ranking problem into the task of identifying “authority nodes on an inferred visual similarity graph and propose an algorithm to analyze the visual link structure that can be created among a group of images. Through an iterative procedure based on the PageRank computation, a numerical weight is assigned to each image; this measures its relative importance to the other images being considered. The incorporation of visual signals in this process di ers from the majority of large-scale commercial-search engines in use today. Commercial search-engines often solely rely on the text clues of the pages in which images are embedded to rank images, and often en-tirely ignore the content of the images themselves as a rank-ing signal. To quantify the performance of our approach in a real-world system, we conducted a series of 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.
PageRank Graph Algorithms Visual Similarity
Yushi Jing Shumeet Baluja
College Of Computing, Georgia Institute of Technology, Atlanta GA;Google, Inc. 1600 Amphitheater Par Google, Inc. 1600 Amphitheater Parkway, Mountain View, CA
国际会议
第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)
北京
英文
2008-04-21（万方平台首次上网日期，不代表论文的发表时间）