1 / 1

Mining Social Images with Distance Metric Learning for Automated Image Tagging

Peilin Zhao ¹ zhao0106 @ntu.edu.sg. Ying He ¹ yhe @ntu.edu.sg. Steven C.H. Hoi ¹ chhoi @ntu.edu.sg. ¹Nanyang Technological University, Singapore. Introduction. Algorithm. CHARTS / GRAPHS / IMAGES.

neola
Download Presentation

Mining Social Images with Distance Metric Learning for Automated Image Tagging

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Peilin Zhao¹ zhao0106@ntu.edu.sg Ying He¹ yhe@ntu.edu.sg Steven C.H. Hoi¹ chhoi@ntu.edu.sg ¹Nanyang Technological University, Singapore Introduction Algorithm CHARTS / GRAPHS / IMAGES Fig. the process of a retrieval-based annotation approach by mining social images with distance metric learning Mining Social Images with Distance Metric Learning for Automated Image Tagging Convergence Analysis Fig. Example of automatically tagging a novel image by UDML. UDML • Basic Ideas of UDML • Exploit both visual and textual contents of social images. • Unify both inductive and transductive metric learning techniques. Experimental Results Pengcheng Wu¹ wupe0003@ntu.edu.sg Fig. Average precision at top t annotated tags under 11 methods • Tagging Images with Optimized Metrics • Retrieve k-nearest neighbors of the novel unlabeled image. • Calculate the frequency of each candidate tag associated with the k-nearest social images. • Assign the unlabeled image with tags of high frequency and smallaverage distance. Fig. Average precision under different top k similar images used Fig. [Examples showing the tagging results by 11 different methods. Fourth ACM International Conference on Web Search and Data Mining(WSDM 2011)

More Related