LEGO-MM: LEarning structured model by probabilistic logic ontology tree for multimedia

Jinhui Tang, Shiyu Chang, Guo Jun Qi, Qi Tian, Yong Rui, Thomas S. Huang

Research output: Contribution to journalArticlepeer-review


Recent advances in multimedia ontology have resulted in a number of concept models, e.g., large-scale concept for multimedia and Mediamill 101, which are accessible and public to other researchers. However, most current research effort still focuses on building new concepts from scratch, very few work explores the appropriate method to construct new concepts upon the existing models already in the warehouse. To address this issue, we propose a new framework in this paper, termed LEarning Structured Model by Probabilistic loGic Ontology Tree for MultiM edia (LEGO 1 -MM), which can seamlessly integrate both the new target training examples and the existing primitive concept models to infer the more complex concept models. LEGO-MM treats the primitive concept models as the lego toy to potentially construct an unlimited vocabulary of new concepts. Specifically, we first formulate the logic operations to be the lego connectors to combine the existing concept models hierarchically in probabilistic logic ontology trees. Then, we incorporate new target training information simultaneously to efficiently disambiguate the underlying logic tree and correct the error propagation. Extensive experiments are conducted on a large vehicle domain data set from ImageNet. The results demonstrate that LEGO-MM has significantly superior performance over the existing state-of-the-art methods, which build new concept models from scratch.

Original languageEnglish (US)
Article number7574383
Pages (from-to)196-207
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number1
StatePublished - Jan 2017


  • concept recycling
  • logical operations
  • model warehouse
  • probabilistic logic ontology tree

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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