Learning graph structure for multi-label image classification via clique generation

Mingkui Tan, Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Junbin Gao, Fuyuan Hu, Zhen Zhang

Research output: Book chapter/Published conference paperConference paperpeer-review

27 Citations (Scopus)
5 Downloads (Pure)


Exploiting label dependency for multi-label image classification can significantly improve classification performance. Probabilistic Graphical Models are one of the primary methods for representing such dependencies. The structure of graphical models, however, is either determined heuristically or learned from very limited information. Moreover, neither of these approaches scales well to large or complex graphs. We propose a principled way to learn the structure of a graphical model by considering input features and labels, together with loss functions. We formulate this problem into a max-margin framework initially, and then transform it into a convex programming problem. Finally, we propose a highly scalable procedure that activates a set of cliques iteratively. Our approach exhibits both strong theoretical properties and a significant performance improvement over state-of-the-art methods on both synthetic and real-world data sets.
Original languageEnglish
Title of host publicationCVPR 2015
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages10
Publication statusPublished - 2015
EventIEEE Conference on Computer Vision and Pattern Recognition - Boston, New Zealand
Duration: 07 Jun 201512 Jun 2015


ConferenceIEEE Conference on Computer Vision and Pattern Recognition
CountryNew Zealand

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