TY - GEN
T1 - Iteratively clustering web images based on link and attribute reinforcements
AU - Wang, Xin-Jing
AU - Ma, Wei-Ying
AU - Zhang, Lei
AU - Li, Xing
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - Image clustering is an important research topic which contributes to a wide range of applications. Traditional image clustering approaches are based on image content features only, while content features alone can hardly describe the semantics of the images. In the context of Web, images are no longer assumed homogeneous and "flat" distributed but are richly structured. There are two kinds of reinforcements embedded in such data: 1) the reinforcement between attributes of different data types (intra-type links reinforcements); and 2) the reinforcement between object attributes and the inter-type links (inter-type links reinforcements). Unfortunately, most of the previous works addressing relational data failed to fully explore the reinforcements. In this paper, we propose a reinforcement clustering framework to tackle this problem. It reinforces images and texts' attributes via inter-type links and inversely uses these attributes to update these links. The iterative reinforcing nature of this framework promises the discovery of the semantic structure of images, which is the basis of image clustering. Experimental results show the effectiveness of our proposed framework. Copyright © 2005 ACM.
AB - Image clustering is an important research topic which contributes to a wide range of applications. Traditional image clustering approaches are based on image content features only, while content features alone can hardly describe the semantics of the images. In the context of Web, images are no longer assumed homogeneous and "flat" distributed but are richly structured. There are two kinds of reinforcements embedded in such data: 1) the reinforcement between attributes of different data types (intra-type links reinforcements); and 2) the reinforcement between object attributes and the inter-type links (inter-type links reinforcements). Unfortunately, most of the previous works addressing relational data failed to fully explore the reinforcements. In this paper, we propose a reinforcement clustering framework to tackle this problem. It reinforces images and texts' attributes via inter-type links and inversely uses these attributes to update these links. The iterative reinforcing nature of this framework promises the discovery of the semantic structure of images, which is the basis of image clustering. Experimental results show the effectiveness of our proposed framework. Copyright © 2005 ACM.
KW - Image clustering
KW - Iterative reinforcement
KW - Link mining
UR - https://www.scopus.com/pages/publications/84883072971
UR - https://www.scopus.com/pages/publications/84883072971#tab=impact
U2 - 10.1145/1101149.1101168
DO - 10.1145/1101149.1101168
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1595930442
SN - 9781595930446
T3 - Proceedings of the 13th ACM International Conference on Multimedia, MM 2005
SP - 122
EP - 131
BT - Proceedings of the 13th ACM International Conference on Multimedia, MM 2005
T2 - 13th ACM International Conference on Multimedia, MM 2005
Y2 - 6 November 2005 through 11 November 2005
ER -