Personalized image annotation via class-specific cross-domain learning

In this paper, we propose to learn users' own profiles for image annotation with the purpose of facilitating image searching towards users' intentions. Considering that the size of a user's annotation vocabulary is usually small and different users have different visual understanding towards a specific tag, we perform personalization in a class-specific form to summarize a user's annotation profiles for each tag in his vocabulary. In particular, we first exploit a generic annotation dataset with a class-specific weighted nearest neighbor (cs-WNN) model by combining the techniques of multiple kernel learning and nearest neighbor modeling. Next, a new personalization method, namely class-specific cross-domain learning (cs-CDL), is proposed to achieve users' own annotation profiles (i.e. the user-specific parameters of cs-WNN models) by exploiting users' annotation datasets. Experimental results have been reported over several challenging image databases to validate the effectiveness of the proposed method for both generic and personalized image annotation. (C) 2015 Elsevier B.V. All rights reserved.