LCMKL: latent-community and multi-kernel learning based image annotation

Authors:
Qing Li;Yun Gu;Xueming Qian
Affiliations:
SMILES LAB, Xi'an Jiaotong University, Xi'an, China;SMILES LAB, Xi'an Jiaotong Univeristy, Xi'an, China;SMILES LAB, Xi'an Jiaotong University, Xi'an, China
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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Abstract

Automatic image annotation is an important function for online photo sharing service. The concurrence of labels is pretty common in multi-label annotation. In this paper, we propose a novel approach called latent-community and multi-kernel learning (LCMKL). The established graph of labels is regarded as a semantic network. Community detection method is introduced that treats the label set as communities. Multi-kernel learning SVM is adopted for specifying communities and settling difficulty of extracting semantically meaningful entities with some simple features. Experiments on NUS-WIDE database demonstrate that LCMKL outperforms other state-of-the-art approaches.