Jump to : Download | Abstract | Contact | BibTex reference | EndNote reference |


Yu-Gang Jiang, Qi Dai, Jun Wang, Chong-Wah Ngo, Xiangyang Xue, Shih-Fu Chang. Fast Semantic Diffusion for Large Scale Context-Based Image and Video Annotation. IEEE Transactions on Image Processing, 21:3080-3091, 2012.

Download [help]

Download paper: Adobe portable document (pdf)

Copyright notice:This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


Exploring context information for visual recognition has recently received significant research attention. This paper proposes a novel and highly efficient approach, named semantic diffusion (SD), to utilize semantic context for large scale image and video annotation. Starting from the initial annotation of a large number of semantic concepts (categories), obtained by either machine learning or manual tagging, the proposed approach refines the results using graph diffusion technique, which recovers the consistency and smoothness of the annotations over a semantic graph. Different from the existing graph-based learning methods that model relations among data samples, the semantic graph captures context by treating the concepts as nodes and the concept affinities as the weights of edges. In particular, our approach is capable of simultaneously improving annotation accuracy and adapting the concept affinities to new test data. The adaptation provides a means to handle domain change between training and test data, which often occurs in practice. Extensive experiments are conducted to improve concept annotation results using Flickr images and TV program videos. Results show consistent and significant performance gain (10+% on both image and video datasets). Source codes of the proposed algorithms are available online [1]


Yu-Gang Jiang
Jun Wang
Shih-Fu Chang

BibTex Reference

   Author = {Jiang, Yu-Gang and Dai, Qi and Wang, Jun and Ngo, Chong-Wah and Xue, Xiangyang and Chang, Shih-Fu},
   Title = {Fast Semantic Diffusion for Large Scale Context-Based Image and Video Annotation},
   Journal = {IEEE Transactions on Image Processing},
   Volume = {21},
   Pages = {3080--3091},
   Year = {2012}

EndNote Reference [help]

Get EndNote Reference (.ref)


For problems or questions regarding this web site contact The Web Master.

This document was translated automatically from BibTEX by bib2html (Copyright 2003 © Eric Marchand, INRIA, Vista Project).