VISUAL ATTENTION INSPIRED DISTANT VIEW AND CLOSE-UP VIEW CLASSIFICATION

The images of distant view and close-up view indicate a photographers' attention which can be further utilized for user behavior analysis and scene evaluation. As images may compose arbitrary contexts, distant view and close-up view classification becomes non-trivial. In this work, we found two cues can represent human visual attention, i.e. focus cue and scale cue. We model the focus cue in frequency domain using the Discrete Wavelet Transform, and employ signal distribution as the focus feature. For the scale cue, we model it by defining a spatial size and a conceptual size in the image using the Edge Box and Convolution Neural Network. By integrating these two models, a robust scheme is proposed for this non-trivial task. Experiments on a newly retrieved dataset, which has 2137 natural images, show the classification accuracy achieves up to 97.3%.