Towards unsupervised attention object extraction by integrating visual attention and object growing
Content-related functionalities of image/video applications call for efficient tools that can automatically extract meaningful objects from images. However, traditional methods generally fail to capture objects of user interest because they totally neglect human visual attention perception. Aiming to address this problem, this study proposes a generic model for unsupervised extraction of viewer's attention objects from color images. We formulate the attention objects as a Markov random field (MRF). Then, the MRF is expressed in the form of a Gibbs random field with an energy function. The energy minimization that integrates visual attention and object growing provides a practical way to obtain attention objects. The proposed model works in a manner analogous to humans and has great promise to be a basic tool for content-based image/video applications. Experimental results show the effectiveness of the proposed model.