Pagerank for product image search Export

@inproceedings{citeulike:2811664, abstract = {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.}, address = {New York, NY, USA}, author = {Jing, Yushi and Baluja, Shumeet}, booktitle = {WWW '08: Proceeding of the 17th international conference on World Wide Web}, citeulike-article-id = {2811664}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1367497.1367540}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1367497.1367540}, comment = {Builds a graph in which nodes are images and links measure similarity in terms of visual features.}, doi = {10.1145/1367497.1367540}, isbn = {978-1-60558-085-2}, keywords = {graph, image, ranking}, location = {Beijing, China}, pages = {307--316}, posted-at = {2008-09-03 17:21:13}, priority = {0}, publisher = {ACM}, title = {Pagerank for product image search}, url = {http://dx.doi.org/10.1145/1367497.1367540}, year = {2008} }

TY - CONF ID - citeulike:2811664 L3 - citeulike-article-id:2811664 N2 - 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. CY - Beijing, China SN - 978-1-60558-085-2 AD - New York, NY, USA EP - 316 TI - Pagerank for product image search PB - ACM T2 - WWW '08: Proceeding of the 17th international conference on World Wide Web SP - 307 KW - graph KW - image KW - ranking N1 - Builds a graph in which nodes are images and links measure similarity in terms of visual features. AU - Jing, Yushi AU - Baluja, Shumeet PY - 2008/// UR - http://dx.doi.org/10.1145/1367497.1367540 ER -