JustClick: Personalized Image Recommendation via Exploratory Search from Large-Scale Flickr Image Collections
Jianping Fan, Daniel A. Keim, Yuli Gao, Hangzai Luo, Zongmin Li · 2008
In this paper, we have developed a novel framework called JustClick to enable personalized image recommendation via exploratory search from large-scale collections of manually- annotated Flickr images. First, a topic network is automatically generated to summarize large-scale collections of manually- annotated Flickr images at a semantic level. Hyperbolic visu- alization is further used to enable interactive navigation and exploration of the topic network, so that users can gain insights of large-scale image collections at the first glance, build up their mental query models interactively and specify their queries (i.e., image needs) more precisely by selecting the image topics on the topic network directly. Thus our personalized query recommendation framework can effectively address both the problem of query formulation and the problem of vocabulary discrepancy and null returns. Second, a limited number of images are automatically recommended as the most represen- tative images according to their representativeness for a given image topic. Kernel principal component analysis and hyperbolic visualization are seamlessly integrated to organize and layout the recommended images (i.e., most representative images) according to their nonlinear visual similarities, so that users can assess the relevance between the recommended images and their real query intentions interactively. An interactive interface is implemented to allow users to express their time-varying query intentions and to direct the system to more relevant images according to their personal preferences. Our experiments on large-scale collections of Flickr image collections show very positive results.