Abstract
Although automatic fuzzy clustering framework (AFCF) based on improved density peak clustering is able to achieve automatic and efficient image segmentation, the framework suffers from two problems. The first one is that the adaptive morphological reconstruction (AMR) employed by the AFCF is easily influenced by the initial structuring element. The second one is that the improved density peak clustering using a density balance strategy is complex for finding potential clustering centers. To address these two problems, we propose a fast and automatic image segmentation algorithm using superpixel-based graph clustering (FAS-SGC). The proposed algorithm has two major contributions. First, the AMR based on regional minimum removal (AMR-RMR) is presented to improve the superpixel result generated by the AMR. The binary morphological reconstruction is performed on a regional minimum image, which overcomes the problem that the initial structuring element of the AMR is chosen empirically, since the geometrical information of images is effectively explored and utilized. Second, we use an eigenvalue gradient clustering (EGC) instead of improved density peak (DP) algorithms to obtain potential clustering centers, since the EGC is faster and requires fewer parameters than the DP algorithm. Experiments show that the proposed algorithm is able to achieve automatic image segmentation, providing better segmentation results while requiring less execution time than other state-of-the-art algorithms.
Highlights
I MAGE segmentation has been widely used in computer vision [1], remote sensing image analysis [2], biomedical research [3], industrial detection [4], etc
In our previous work [27], we proposed a novel adaptive morphological reconstruction algorithm that is useful for seeded image segmentation
We study automatic graph clustering based on superpixels provided by the adaptive morphological reconstruction (AMR)-RMR-WT
Summary
I MAGE segmentation has been widely used in computer vision [1], remote sensing image analysis [2], biomedical research [3], industrial detection [4], etc. A popular idea is to use neighboring information to replace its central pixel Based on this idea, researchers proposed many improved clustering algorithms by employing a neighboring window of fixed size. As it is unreasonable to use a neighboring window with fixed shape and size to incorporate local spatial information, those aforementioned algorithms have a limited capability for improving image segmentation effect [20] To address this issue, researchers incorporated adaptive neighboring information into objective functions such as Liu’s algorithm [21], superpixel-based clustering algorithm (SFFCM) [22], a fuzzy double c-means clustering based on sparse selfrepresentation (FDCM-SSR [23]), and sparse learning based FCM [24]. We propose a fast and automatic image segmentation algorithm employing superpixel-based graph clustering (FAS-SGC).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have