Automatic Fuzzy Clustering Framework for Image Segmentation

Abstract

Clustering algorithms by minimizing an object function share a clear drawback that the number of clusters need to be set manually. Although density peak clustering is able to seek the number of clusters, it suffers from memory overflow when it is used for image segmentation because a moderate-size image usually includes a large number of pixels leading to a huge similarity matrix. To address the issue, here we proposed an automatic fuzzy clustering framework (AFCF) for image segmentation. The proposed framework has threefold contributions. Firstly, the idea of superpixel is used for the density peak (DP) algorithm, which efficiently reduces the size of the similarity matrix and thus improves the computational efficiency of the DP algorithm. Secondly, we employ a density balance algorithm to obtain a more robust decision-graph that helps the DP algorithm to achieve fully automatic clustering. Finally, a fuzzy c-means clustering based on prior entropy is used in the framework to improve image segmentation results. Because the spatial neighboring information of both the pixels and membership are considered, the final segmentation result is improved effectively. Experiments show that the proposed framework is not only able to achieve automatic image segmentation, it also provides better segmentation results than state-of-the-art algorithms.Index Terms-Fuzzy clustering, image segmentation, superpixel, density peak (DP) algorithm Tao Lei (M'17) received the Ph.D degree in Information and Communication Engineering from Northwestern

Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering

“…In this paper, nine popular clustering-based image segmentation algorithms are considered. They are HMRF-FCM [46], FLICM [11], KWFLICM [13], Liu's algorithm [21], FRFCM [19], DSFCM_N [14], FNCut [28], SFFCM [22], and AFCF [36]. All these comparative algorithms and experimental evaluation are implemented with MATLAB 2018b and performed on a DELL desktop with Intel(R) Core (TM) CPU, i7-6700, 3.4GHz, 16GB RAM.…”

“…In order to apply DP algorithm to automatic image segmentation, Lei et al [36] employ superpixel and a density balance algorithm to improve the DP algorithm. In this algorithm, authors use superpixel to overcome the problem of memory overflow for large-scale images.…”

“…However, it only provides a decision graph without giving the number of clusters. To apply the DP algorithm to automatic image segmentation, Lei et al [36] proposed an automatic fuzzy clustering framework (AFCF) by employing superpixel and a density balance algorithm to obtain better segmentation results automatically. More automatic clustering approaches can be seen in [37][38][39].…”

A survey on matching strategies for boundary image comparison and evaluation

“…Two different methods have been selected for the generation of the boundary images: the superpixel-based clustering approach by Lei et al [79] , a modern method adhering to current trends in boundary detection and segmentation, and the Canny method [80] , a well-known representative of gradient-based boundary detection methods.…”

Robust Self-Sparse Fuzzy Clustering for Image Segmentation

“…Therefore, a middle-size window like 3 × 3 or 5 × 5 is popular for these algorithms since the window achieves a balance between spatial information and computational cost. To overcome the limitation of fixed-size windows, some researchers employ superpixel techniques to obtain adaptive neighboring information such as Liu's algorithm [35], FDCM_SSR [36], SFFCM [37], AFCA [38], etc. Although the second strategy improves segmentation accuracy due to the utilization of adaptive neighboring information, these algorithms seriously depend on the selection of superpixel algorithms [39][40][41].…”