Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation

Abstract

A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely used for grayscale and color image segmentation. However, most of them are timeconsuming and unable to provide desired segmentation results for color images due to two reasons. The first one is that the incorporation of local spatial information often causes a high computational complexity due to the repeated distance computation between clustering centers and pixels within a local neighboring window. The other one is that a regular neighboring window usually breaks up the real local spatial structure of images and thus leads to a poor segmentation. In this work, we propose a superpixel-based fast FCM clustering algorithm (SFFCM) that is significantly faster and more robust than stateof-the-art clustering algorithms for color image segmentation. To obtain better local spatial neighborhoods, we firstly define a multiscale morphological gradient reconstruction (MMGR) operation to obtain a superpixel image with accurate contour. In contrast to traditional neighboring window of fixed size and shape, the superpixel image provides better adaptive and irregular local spatial neighborhoods that are helpful for improving color image segmentation. Secondly, based on the obtained superpixel image, the original color image is simplified efficiently and its histogram is computed easily by counting the number of pixels in each region of the superpixel image. Finally, we implement FCM with histogram parameter on the superpixel image to obtain the final segmentation result. Experiments performed on synthetic images and real images demonstrate that the proposed algorithm provides better segmentation results and takes less time than state-of-the-art clustering algorithms for color image segmentation.

Automatic Fuzzy Clustering Framework for Image Segmentation

“…Based on the superpixel result of an image, one can use a pixel to replace all pixels in a superpixel area to efficiently reduce the number of pixels in an image. Motivated by this, Lei et al proposed SFFCM [23] for color image segmentation. SFFCM addresses two difficulties existing in clustering algorithms for color image segmentation.…”

“…Fig. 6 shows superpixel results provided by different superpixel algorithms such as SLIC [41], DBSCAN [42], LSC [43], GMMSP [44], HS [45], and MMGR-WT [23]. Note that each of SLIC, DBSCAN, LSC, and HS requires one parameter, i.e., the number of superpixel area; GMMSP also requires one parameter that is the size of areas; but MMGR-WT needs two parameters that are the initial structuring element denoted by r 1 and the minimal threshold error denoted by η.…”

“…Table I shows the number of areas in different superpixel images for an original image size of 321×481. In practical applications, η is usually a constant and η = 10 −4 in [23]. We set η = 0.1 here in order to obtain more superpixel areas for fair comparison.…”

“…Unfortunately, FDCM SSR still has a higher computational complexity than most of popular algorithms. Inspired by superpixel technology [22] and EnFCM, Lei et al [23] proposed a superpixel-based fast FCM algorithm (SFFCM) for color image segmentation. SFFCM has two advantages.…”

Development of Machine Vision System for Pen Parts Identification under Various Illumination Conditions in an Industry 4.0 Environment

“…FCM clustering is derived based on the idea of uncertainty of belonging, using a membership grading, and can be more instinctive in comparison to hard clustering [43]. Lei et al [44] presented a Superpixel-based Fast FCM algorithm (SFFCM) for color-based image segmentation. Through the proposed algorithm, watershed transformation based on multi-scale morphological reconstruction integrated the color histogram of superpixel results in the objective function of FCM to improve the clustering result and decrease computational time.…”