Search-Based Automatic Image Annotation via Flickr Photos Using Tag Expansion

  title={Search-Based Automatic Image Annotation via Flickr Photos Using Tag Expansion},
  author={Liang-Chi Hsieh and Winston H. Hsu},
Exponentially growing photo collections motivate the needs for automatic image annotation for effective manipulations (e.g., search, browsing). Most of the prior works rely on supervised learning approaches and are not practical due to poor performance, out-ofvocabulary problem, and being time-consuming in acquiring training data and learning. In this work, we argue automatic image annotation by search over user-contributed photo sites (e.g., Flickr), which have accumulated rich human knowledge… CONTINUE READING


Publications citing this paper.
Showing 1-9 of 9 extracted citations

A Negative Sample Image Selection Method Referring to Semantic Hierarchical Structure for Image Annotation

2013 International Conference on Signal-Image Technology & Internet-Based Systems • 2013
View 7 Excerpts
Highly Influenced

Capturing the visual language of social media

2015 IEEE International Conference on Multimedia and Expo (ICME) • 2015
View 2 Excerpts

Artificial Intelligence Applications and Innovations

IFIP Advances in Information and Communication Technology • 2014
View 1 Excerpt

Image Searching Using AutoAnnotation

Vineetha Linga, S. Kulkarni, Ashish Babel, Rutuja Dhumal, Mr. Laxman Deokate


Publications referenced by this paper.
Showing 1-9 of 9 references

AnnoSearch: Image Auto-Annotation by Search

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) • 2006
View 7 Excerpts
Highly Influenced

Scalable search-based image annotation of personal images

Multimedia Information Retrieval • 2006
View 5 Excerpts
Highly Influenced

Annotating images by harnessing worldwide user-tagged photos

2009 IEEE International Conference on Acoustics, Speech and Signal Processing • 2009

What, where and who? Classifying events by scene and object recognition

2007 IEEE 11th International Conference on Computer Vision • 2007
View 1 Excerpt

Similar Papers

Loading similar papers…