Deep Texture-Aware Features for Camouflaged Object Detection

Camouflaged object detection is a challenging task that aims to identify objects having similar texture the surroundings. This paper presents amplify subtle difference between camouflaged and background for by formulating multiple texture-aware refinement modules learn features in deep convolutional neural network. The module computes biased co-variance matrices of feature responses extract information, adopts an affinity loss set parameter maps help separate background, leverages boundary-consistency explore structures details. We evaluate our network on benchmark datasets both qualitatively quantitatively. Experimental results show approach outperforms various state-of-the-art methods large margin.