Bottom-up unsupervised image segmentation using FC-Dense u-net based deep representation clustering and multidimensional feature fusion based region merging

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.