TY - GEN
T1 - Web image annotation using two-step filtering on social tags
AU - Cho, Sunyoung
AU - Cha, Jaeseong
AU - Byun, Hyeran
PY - 2011
Y1 - 2011
N2 - Web image annotation has become an important issue with exploding web images and the necessity of effective image search. The social tags have recently utilized at image annotation because they can reflect the user's tagging tendency, and reduce the semantic gap. However, an effective filtering procedure is required to extract the relevant tags since the user's subjectivity and noisy tags. In this paper, we propose a two-step filtering on social tags for image annotation. This method conducts the filtering and verification tasks by analyzing the tags of visual neighbor images using voting method and co-occurrence analysis. Our method consists of the following three steps: 1) the tag candidate set is founded by searching the visual neighbor images, 2) from a given tag candidate set, coarse filtering is conducted by tag grouping and voting technique, 3) the dense filtering is conducted by using similarity verification for coarse filtered candidate tag set. To evaluate the performance of our approach, we conduct the experiments on a social-tagged image dataset obtained from Flickr. We compare the annotation accuracy between the voting method and our proposed method. Our experimental results show that our method has an improvement in image annotation.
AB - Web image annotation has become an important issue with exploding web images and the necessity of effective image search. The social tags have recently utilized at image annotation because they can reflect the user's tagging tendency, and reduce the semantic gap. However, an effective filtering procedure is required to extract the relevant tags since the user's subjectivity and noisy tags. In this paper, we propose a two-step filtering on social tags for image annotation. This method conducts the filtering and verification tasks by analyzing the tags of visual neighbor images using voting method and co-occurrence analysis. Our method consists of the following three steps: 1) the tag candidate set is founded by searching the visual neighbor images, 2) from a given tag candidate set, coarse filtering is conducted by tag grouping and voting technique, 3) the dense filtering is conducted by using similarity verification for coarse filtered candidate tag set. To evaluate the performance of our approach, we conduct the experiments on a social-tagged image dataset obtained from Flickr. We compare the annotation accuracy between the voting method and our proposed method. Our experimental results show that our method has an improvement in image annotation.
UR - http://www.scopus.com/inward/record.url?scp=79953015314&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79953015314&partnerID=8YFLogxK
U2 - 10.1117/12.876606
DO - 10.1117/12.876606
M3 - Conference contribution
AN - SCOPUS:79953015314
SN - 9780819484161
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Imaging and Printing in a Web 2.0 World II
T2 - Imaging and Printing in a Web 2.0 World II
Y2 - 26 January 2011 through 27 January 2011
ER -