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- W2021964552 abstract "Image classification is a well-studied and hard problem in computer vision. We extend a proven solution for classifying web spam to handle images. We exploit the link structure of the web graph: a web page related to a given category is normally linked to other pages describing related objects. Our approach combines information from the webgraph structure with semi-supervised learning from all the unlabeled images to create a superior image-classification model for multimedia data. We show that fusing image, text and web-graph features gives a 12% improvement (in the area under the ROC curve) over content features alone in an adult image-classification experiment." @default.
- W2021964552 created "2016-06-24" @default.
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- W2021964552 date "2010-10-25" @default.
- W2021964552 modified "2023-09-27" @default.
- W2021964552 title "Image classification using the web graph" @default.
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- W2021964552 doi "https://doi.org/10.1145/1873951.1874131" @default.
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