document: 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 differs 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 entirely ignore the content of the images themselves as a ranking 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 .
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