K-Means, Mean Shift, and SLIC Clustering Algorithms: A Comparison of Performance in Color-based Skin Segmentation

Commonly used in computer vision, segmentation is grouping pixels into meaningful or perceptually similar regions. In this work, we are going to evaluate the performance of three popular data-clustering algorithms, the K-means, mean shift and SLIC algorithms, in the segmentation of human skin based on color. The K-means algorithm Iteratively aims to group data samples into K clusters, where each sample belongs to the cluster with the nearest mean. The mean shift algorithm is a non- parametric algorithm that clusters data iteratively by finding the densest regions (clusters) in a feature space. An enhanced version of the classic K-means algorithm, the SLIC limits the search region to a small area around the cluster reducing the algorithm complexity to be only dependent on the number of pixels in the image. It also provides control over the compactness of the clusters. Color-based skin segmentation algorithms depend on both a color space at which segmentation is performed and a classification method used to determine whether a pixel is skin or non-skin. We have implemented the K-means, mean shift and SLIC algorithms in the RGB color space to detect human skin. Our method begins by clustering images using these algorithms and then segmenting the clustered regions occupied by skin. Pixels in the clusters are classified as skin or non-skin using the Kovac model. We have evaluated the algorithms' performance on the SFA database (controlled environ- ment) and on another database created for testing on an uncontrolled environment. The performance has been evaluated using time complexity, F1 score, recall, and precision. We have found that on average the mean shift algorithm triumphs over the three algorithms in terms of performance while the SLIC algorithms holds an advantage being the fastest.The K-means algorithm has a good performance when the number of clusters K is between 10 and 15, whereas the mean shift algorithm has good performance when the bandwidth h is between 0.03 and 0.06. The SLIC algorithm maxes out its performance at around k = 100 and the number of clusters can be increased to K = 300 without remarkably increasing the complexity

Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection

In agriculture sector, plant leaf diseases detection plays a significant role. Plant leaf detection is important for food security, avoiding economic downturns due to severe plant losses, and avoiding environmental degradation due to inappropriate disease treatment. The image processing consists of image segmentation and image classification are commonly used to extract the infected part from the uninfected part to identify the types of the diseases. Some of the existing methods of segmentation are K-Means, Otsu’s, edge-based segmentation, watershed segmentation, region growing, mean shift, maxflow mincut (MFMC) graph cut and regional colour segmentation. The performance of the previous segmentation methods, on the other hand, is average due to their disadvantages such as sensitive to noise and unable to process image with reflection. The example of the previous classification methods are ResNets, bag of features, artificial neural network (ANN), support vector machine (SVM), AlexNet, probabilistic neural network (PNN), principal component analysis (PCA) and k-nearest neighbour (k-NN) and they also have an average performance. This is due to instability and complexity of the network. Hence, algorithm that performed better is required. Thus, in this study, image segmentation method of Fuzzy C-Means with YCbCr and image classification method of DenseNet-201 to detect plant leaf diseases is proposed. The results show that the proposed method performed better than the previous methods with 96.81% for segmentation as well as 95.11% for classification and it is discovered to be a good fusion of algorithms to detect plant leaf diseases

Boron Nanoparticle Image Analysis using Machine Learning Algorithms

The effort of digital image processing involves efficient computation aimed at developing an economical, fasterand more accurate, and cost-effective automated system. The objective of this paper is to ascertain and categorizeBoron nanoparticles (BNP) using digital image processing techniques. The spatial features are unsheathed fromthe Boron nanoparticle Transmission Electron Microscope (TEM) images using different segmentationtechniques, namely; Fuzzy C -means (FCM) and K-means. The size of Boron nanoparticles is determined andcategorized based on the area(size) in the microsize.The synthesization and characterization of Boronnanoparticles play an important role as an elementary procedure for the formation of Boron nanoparticles. Theresults are analyzed, interpreted and comparison is done with the manual values to observe the efficacy of theresults. It is observed that the K-means segmentation technique yields a smaller amountof error (5.87%) ascompared with Fuzzy C-mean(16.78%). Hence, it is considered that the K-Means is the most relevantsegmentation technique for Boron nanoparticle image analysis and categorization. The statistical test ofsignificance is applied using the Chi-square testing method (at 5% of significance level) to check the relationshipbetween the manual results and the algorithm results.The proposed study also establishes collaborative researchwork between Chemistry and Computer Science departments to develop computational research on theseplatforms

Image segmentation evaluation using an integrated framework

In this paper we present a general framework we have developed for running and evaluating automatic image and video segmentation algorithms. This framework was designed to allow effortless integration of existing and forthcoming image segmentation algorithms, and allows researchers to focus more on the development and evaluation of segmentation methods, relying on the framework for encoding/decoding and visualization. We then utilize this framework to automatically evaluate four distinct segmentation algorithms, and present and discuss the results and statistical findings of the experiment