Recent advances in system resources provide ease in the applicability of deep learning approaches in computer vision. In this paper, we propose a deep learning-based unsupervised image segmentation approach for natural image segmentation. Image segmentation aims to transform an image into regions, representing various objects in the image. Our method consists of a fully convolutional dense network-based unsupervised deep representation oriented clustering, followed by shallow features based high-dimensional region merging to produce the final segmented image. We evaluate our proposed approach on the BSD300 database and perform a comparison with several classical and some recent deep learning-based unsupervised segmentation methods. The experimental results represent that the proposed method is comparable and confirm the efficacy of the proposed approach.

系统资源的最新进展使深度学习方法在计算机视觉中的应用变得容易。在本文中，我们提出了一种基于深度学习的无监督图像分割方法来进行自然图像分割。图像分割旨在将图像转换成代表图像中各种对象的区域。我们的方法包括基于完全卷积的密集网络的无监督深度表示定向聚类，然后是基于浅特征的高维区域合并以生成最终的分割图像。我们在BSD300数据库上评估了我们提出的方法，并与几种经典的和最近基于深度学习的无监督分割方法进行了比较。