Horror Image Recognition Based on Emotional Attention

Along with the ever-growing Web, people benefit more and more from sharing information. Meanwhile, the harmful and illegal content, such as pornography, violence, horror etc., permeates the Web. Horror images, whose threat to children’s health is no less than that from pornographic content, are nowadays neglected by existing Web filtering tools. This paper focuses on horror image recognition, which may further be applied to Web horror content filtering. The contributions of this paper are two-fold. First, the emotional attention mechanism is introduced into our work to detect emotional salient region in an image. And a top-down emotional saliency computation model is initially proposed based on color emotion and color harmony theories. Second, we present an Attention based Bag-of-Words (ABoW) framework for image’s emotion representation by combining the emotional saliency computation model and the Bag-of-Words model. Based on ABoW, a horror image recognition algorithm is given out. The experimental results on diverse real images collected from internet show that the proposed emotional saliency model and horror image recognition algorithm are effective.