TY - GEN

T1 - An Adaptive Teleportation Random Walk Model for Learning Social Tag Relevance

AU - Zhu, Xiaofei

AU - Nejdl, Wolfgang

AU - Georgescu, Mihai

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Social tags are known to be a valuable source of information for image retrieval and organization. However, contrary to the conventional document retrieval, rich tag frequency in-formation in social sharing systems, such as Flickr, is not available, thus we cannot directly use the tag frequency (analogous to the term frequency in a document) to represent the relevance of tags. Many heuristic approaches have been proposed to address this problem, among which the well-known neighbor voting based approaches are the most effective methods. The basic assumption of these methods is that a tag is considered as relevant to the visual content of a target image if this tag is also used to annotate the visual neighbor images of the target image by lots of different users. The main limitation of these approaches is that they treat the voting power of each neighbor image either equally or simply based on its visual similarity. In this paper, we cast the social tag relevance learning problem as an adaptive teleportation random walk process on the voting graph. In particular, we model the relationships among images by constructing a voting graph, and then propose an adaptive teleportation random walk, in which a confidence factor is introduced to control the teleportation probability, on the voting graph. Through this process, direct and indirect relationships among images can be explored to cooperatively estimate the tag relevance. To quantify the performance of our approach, we compare it with state-of-the-art methods on two publicly available datasets (NUS-WIDE and MIR Flickr). The results indicate that our method achieves sub-stantial performance gains on these datasets.

AB - Social tags are known to be a valuable source of information for image retrieval and organization. However, contrary to the conventional document retrieval, rich tag frequency in-formation in social sharing systems, such as Flickr, is not available, thus we cannot directly use the tag frequency (analogous to the term frequency in a document) to represent the relevance of tags. Many heuristic approaches have been proposed to address this problem, among which the well-known neighbor voting based approaches are the most effective methods. The basic assumption of these methods is that a tag is considered as relevant to the visual content of a target image if this tag is also used to annotate the visual neighbor images of the target image by lots of different users. The main limitation of these approaches is that they treat the voting power of each neighbor image either equally or simply based on its visual similarity. In this paper, we cast the social tag relevance learning problem as an adaptive teleportation random walk process on the voting graph. In particular, we model the relationships among images by constructing a voting graph, and then propose an adaptive teleportation random walk, in which a confidence factor is introduced to control the teleportation probability, on the voting graph. Through this process, direct and indirect relationships among images can be explored to cooperatively estimate the tag relevance. To quantify the performance of our approach, we compare it with state-of-the-art methods on two publicly available datasets (NUS-WIDE and MIR Flickr). The results indicate that our method achieves sub-stantial performance gains on these datasets.

KW - Neighbor voting

KW - Random walk

KW - Social tag relevance

UR - http://www.scopus.com/inward/record.url?scp=84904580435&partnerID=8YFLogxK

U2 - 10.1145/2600428.2609556

DO - 10.1145/2600428.2609556

M3 - Conference contribution

AN - SCOPUS:84904580435

SN - 9781450322591

SP - 223

EP - 232

BT - SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

PB - Association for Computing Machinery (ACM)

T2 - 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014

Y2 - 6 July 2014 through 11 July 2014

ER -