论文标题
通过卷积神经网络的超级像素分割，具有正则信息最大化
Superpixel Segmentation via Convolutional Neural Networks with Regularized Information Maximization
论文作者
论文摘要
我们通过在推理时间内优化一个随机定量的卷积神经网络（CNN），提出了一种无监督的超像素分割方法。我们的方法通过从单个图像中通过CNN生成超像素，而无需任何标签，通过将提出的目标函数最小化以在推理时间内进行超像素分割。与许多现有方法相比，我们的方法有三个优点：（i）利用CNN的图像进行超像素分割，（ii）根据给定的图像自适应地更改超像素的数量，（iii）通过为目标功能增加辅助成本来控制超级像素的属性。我们在BSDS500和SBD数据集上定量和质量地验证我们方法的优势。
We propose an unsupervised superpixel segmentation method by optimizing a randomly-initialized convolutional neural network (CNN) in inference time. Our method generates superpixels via CNN from a single image without any labels by minimizing a proposed objective function for superpixel segmentation in inference time. There are three advantages to our method compared with many of existing methods: (i) leverages an image prior of CNN for superpixel segmentation, (ii) adaptively changes the number of superpixels according to the given images, and (iii) controls the property of superpixels by adding an auxiliary cost to the objective function. We verify the advantages of our method quantitatively and qualitatively on BSDS500 and SBD datasets.
