A Novel Interactive Image Recommendation System

With the repaid development of internet technology, image documents have become an important information source. It is hard for us to retrieve certain images from all available ones. In this paper, we propose an interactive image recommendation system, which firstly uses color histogram feature or Gabor texture feature to express image contents, then a kernel based K-meanse is utilized to cluster images into multiple classes by their visual features, finally based on a sample and hyperbolic techniques, images are recommended and displayed. The experimental results demonstrate that the proposed system can recommend and display the similar images from the same class efficiently, when users click on the images they are interested in.