Partitioning an image into superpixels based on the similarity of pixels with respect to features such as colour or spatial location can significantly reduce data complexity and improve subsequent image processing tasks. Initial algorithms for unsupervised superpixel generation solely relied on local cues without prioritizing significant edges over arbitrary ones. On the other hand, more recent methods based on unsupervised deep learning either fail to properly address the trade-off between superpixel edge adherence and compactness or lack control over the generated number of superpixels. By using random images with strong spatial correlation as input, \ie, blurred noise images, in a non-convolutional image decoder we can reduce the expected number of contrasts and enforce smooth, connected edges in the reconstructed image. We generate edge-sparse pixel embeddings by encoding additional spatial information into the piece-wise smooth activation maps from the decoder's last hidden layer and use a standard clustering algorithm to extract high quality superpixels. Our proposed method reaches state-of-the-art performance on the BSDS500, PASCAL-Context and a microscopy dataset.
翻译:根据颜色或空间位置等特征的相似性将图像分割成超级像素,可以大大降低数据复杂性,改进随后图像处理任务。 未经监督的超级像素生成的初始算法仅依赖于本地线索,而没有将显著边缘排在任意图像之上。 另一方面,基于未经监督的深层学习的最新方法要么未能适当解决超级像素的坚持和紧凑性之间的平衡,要么对生成的超级像素数量缺乏控制。通过使用具有强空间相关性的随机图像作为输入、 \ie、 模糊的噪音图像,在非演化图像解码器中,我们可以减少对比的预期数量,并在重建后的图像中执行平滑的连接边缘。 我们通过将更多空间信息编码到解密层最后隐藏层的平滑动图中,并使用标准组算法提取高质量的超级像素。 我们提出的方法可以在 BSDS500、 PCAL- Costext 和 微镜像镜像上达到状态的性能。