Visual Attention Prediction (VAP) is a significant and imperative issue in the field of computer vision. Most of existing VAP methods are based on deep learning. However, they do not fully take advantage of the low-level contrast features while generating the visual attention map. In this paper, a novel VAP method is proposed to generate visual attention map via bio-inspired representation learning. The bio-inspired representation learning combines both low-level contrast and high-level semantic features simultaneously, which are developed by the fact that human eye is sensitive to the patches with high contrast and objects with high semantics. The proposed method is composed of three main steps: 1) feature extraction, 2) bio-inspired representation learning and 3) visual attention map generation. Firstly, the high-level semantic feature is extracted from the refined VGG16, while the low-level contrast feature is extracted by the proposed contrast feature extraction block in a deep network. Secondly, during bio-inspired representation learning, both the extracted low-level contrast and high-level semantic features are combined by the designed densely connected block, which is proposed to concatenate various features scale by scale. Finally, the weighted-fusion layer is exploited to generate the ultimate visual attention map based on the obtained representations after bio-inspired representation learning. Extensive experiments are performed to demonstrate the effectiveness of the proposed method.
翻译:视觉关注预测(VAP)是计算机视觉领域一个重要和迫切的问题。现有的VAP方法大多以深层次学习为基础。但是,它们并没有充分利用低水平对比特征,而是充分利用低层次对比特征,同时生成视觉关注地图。在本文中,建议采用新的VAP方法,通过生物激励的演示学习产生视觉关注地图。生物激励的演示学习同时结合了低层次对比和高层次语义特征。生物激励的演示学习同时结合了低层次对比和高层次语义特征,其原因是人类眼睛对高对比和高语义物体的缝隙很敏感。拟议的方法由三个主要步骤组成:1)特征提取,2)生物激励的演示和3)视觉关注映射。首先,从精细的VGGG16中提取了高层次的语义特征,而低层次对比特征则由深层次的对比特征提取。第二,在生物激励的演示期间,提取的低层次对比和高层次语义特征由设计为密密密相连的块组合而设计,拟议将不同层次的特征相交配成以比例的图像展示。最后,在进行深度的模拟的模拟后进行模拟的模拟研究,在进行模拟的模拟中进行模拟研究。