Estimating Viewed Image Categories from fMRI Activity via Multi-view Bayesian Generative Model

Researches for estimating what people view from their brain activity have attracted wide attention. Many existing methods focus on only relationship between brain activity and visual features extracted from viewed images. In this paper, we propose a multi-view Bayesian generative model (MVBGM), which adopts a new view, i.e., category features obtained from viewed images. MVBGM based on automatic feature selection under the Bayesian approach can also avoid overfitting caused by high dimensional features. Experimental results show that MVBGM can estimate viewed image categories from brain activity more accurately than existing methods.