Personalized image annotation via class-specific cross-domain learning
作者：
Highlights：
• ●We present a class-specific weighted nearest neighbour (cs-WNN) model for generic image annotation by directly maximizing the log-likelihood of class-specific tag predictions.●Class-specific modeling and multiple kernel learning are integrated to exploit the nonlinearity of features in the visual space of each tag.●We present a novel personalization method, namely class-specific cross-domain learning (cs-CDL), to build users’ own annotation profiles that correspond to the user-specific parameters of cs-WNN models.●The effectiveness of our proposed method has been validated on three standard datasets and two new generated datasets.
摘要
●We present a class-specific weighted nearest neighbour (cs-WNN) model for generic image annotation by directly maximizing the log-likelihood of class-specific tag predictions.●Class-specific modeling and multiple kernel learning are integrated to exploit the nonlinearity of features in the visual space of each tag.●We present a novel personalization method, namely class-specific cross-domain learning (cs-CDL), to build users’ own annotation profiles that correspond to the user-specific parameters of cs-WNN models.●The effectiveness of our proposed method has been validated on three standard datasets and two new generated datasets.
论文关键词：Personalized image annotation,Cross-domain learning,Nearest neighbor model,Multiple kernel learning
论文评审过程：Received 29 September 2014, Revised 31 January 2015, Accepted 23 March 2015, Available online 30 March 2015.
论文官网地址：https://doi.org/10.1016/j.image.2015.03.008
