Object segmentation in the deep neural network feature domain from highly cluttered natural scenes
Hayder Abdulameer Yousif, Zhihai He, Roland Kays · 2017
Deep convolutional neural networks (DCNNs) offer an effective hierarchical representation of images for various vision analysis tasks, including classification and detection. In this paper, we propose to study background modeling and object segmentation from highly cluttered natural scenes in the DCNN feature domain instead of traditional pixel domain. Specifically, we first design and train a DCNN for animal-human-background object classification, which is used to analyze the input image to generate multi-layer feature maps, representing the responses of different image regions to the animal-human-background classifier. From these feature maps, we construct the so-called deep objectness graph for accurate animal-human object segmentation with graph cut. The segmented object regions from each image in the sequence are then verified and fused in the temporal domain using background modeling. Recognizing that the DCNN is very computation-intensive, we explore a fast and efficient design of the DCNN which finds a good trade-off between complexity and the classification-segmentation performance. Our experimental results demonstrate that our proposed method outperforms existing state-of-the-art methods on the camera-trap dataset with highly cluttered natural scenes.