Featured correspondence topic model for semantic search on social image collections
作者：
Highlights：
• A new framework to retrieve semantically relevant images from the social database.
• Probabilistic topic model to predict the missing tags and remove the noisy ones.
• Two algorithms for the estimation of model parameters and tag correspondence.
• The scoring scheme relies on the fusion of visual and textual information.
• The outperformance of image annotation and retrieval to state-of-the-art methods.
摘要
•A new framework to retrieve semantically relevant images from the social database.•Probabilistic topic model to predict the missing tags and remove the noisy ones.•Two algorithms for the estimation of model parameters and tag correspondence.•The scoring scheme relies on the fusion of visual and textual information.•The outperformance of image annotation and retrieval to state-of-the-art methods.
论文关键词：Image retrieval,Image annotation,Social image tagging,Topic modeling,Probabilistic graphical model
论文评审过程：Received 20 June 2016, Revised 12 January 2017, Accepted 27 January 2017, Available online 31 January 2017, Version of Record 8 February 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.01.055
