Effective image clustering based on human mental search

Image segmentation is one of the fundamental techniques in image analysis. One group of segmentation techniques is based on clustering principles, where association of image pixels is based on a similarity criterion. Conventional clustering algorithms, such as k-means, can be used for this purpose but have several drawbacks including dependence on initialisation conditions and a higher likelihood of converging to local rather than global optima. In this paper, we propose a clustering-based image segmentation method that is based on the human mental search (HMS) algorithm. HMS is a recent metaheuristic algorithm based on the manner of searching in the space of online auctions. In HMS, each candidate solution is called a bid, and the algorithm comprises three major stages: mental search, which explores the vicinity of a solution using Levy flight to find better solutions; grouping which places a set of candidate solutions into a group using a clustering algorithm; and moving bids toward promising solution areas. In our image clustering application, bids encode the cluster centres and we evaluate three different objective functions. In an extensive set of experiments, we compare the efficacy of our proposed approach with several state-of-the-art metaheuristic algorithms including a genetic algorithm, differential evolution, particle swarm optimisation, artificial bee colony algorithm, and harmony search. We assess the techniques based on a variety of metrics including the objective functions, a cluster validity index, as well as unsupervised and supervised image segmentation criteria. Moreover, we perform some tests in higher dimensions, and conduct a statistical analysis to compare our proposed method to its competitors. The obtained results clearly show that the proposed algorithm represents a highly effective approach to image clustering that outperforms other state-of-the-art techniques

A transfer learning based artificial neural network in geometrical design of textured surfaces for tribological applications

This study aims at introducing the potential to utilise transfer learning methods in the training of artificial neural networks for tribological applications. Artificially enhanced surfaces through surface texturing, as an example, are investigated under hydrodynamic regime of lubrication. The performance of these surface features is assessed in terms of load carrying capacity and friction. A large performance dataset including bearing load carrying capacity and friction is initially obtained for a specific category of textures with rectangular cross-sectional profile through analytical methods. The produced bearing performance are used to train a neural network. This neural network was then trained further by a minimal set of performance measure data from an intended category of textures with triangular cross-sectional profiles. It is shown that the resulting neural network performs with acceptable level of confidence for those intended texture profiles when trained with such relatively low number of performance data points. The results indicate that fast analytical methods can potentially produce a large volume of training datasets, which effectively allows for use of relatively lower number of training data sets from the intended category, where creating data for trainings can be more complex or time consuming. Use of transfer learning method in tribological applications and use of bearing performance parameters, as opposed to bearing design parameters, for training the neural networks are the major novel contributions of this study, which has not hitherto been reported elsewhere.</p

Automatic clustering using a local search-based human mental search algorithm for image segmentation

Clustering is a commonly employed approach to image segmentation. To overcome the problems of conventional algorithms such as getting trapped in local optima, in this paper, we propose an improved automatic clustering algorithm for image segmentation based on the human mental search (HMS) algorithm, a recently proposed method to solve complex optimisation problems. In contrast to most existing methods for image clustering, our approach does not require any prior knowledge about the number of clusters but rather determines the optimal number of clusters automatically. In addition, for further improved efficacy, we incorporate local search operators which are designed to make changes to the current cluster configuration. To evaluate the performance of our proposed algorithm, we perform an extensive comparison with several state-of-the-art algorithms on a benchmark set of images and using a variety of metrics including cost function, correctness of the obtained numbers of clusters, stability, as well as supervised and unsupervised segmentation criteria. The obtained results clearly indicate excellent performance compared to existing methods with our approach yielding the best result in 16 of 17 cases based on cost function evaluation, 9 of 11 cases based on number of identified clusters, 13 of 17 cases based on the unsupervised Borsotti image segmentation criterion, and 7 of 11 cases based on the supervised PRI image segmentation metric

A memetic imperialist competitive algorithm with chaotic maps for multi-layer neural network training

The performance of artificial neural networks (ANNs) is largely dependent on the success of the training process. Gradient descent-based methods are the most widely used training algorithms but have drawbacks such as ending up in local minima. One approach to overcome this is to use population-based algorithms such as the imperialist competitive algorithm (ICA) which is inspired by the imperialist competition between countries. In this paper, we present a new memetic approach for neural network training to improve the efficacy of ANNs. Our proposed approach – Memetic Imperialist Competitive Algorithm with Chaotic Maps (MICA-CM) – is based on a memetic ICA and chaotic maps, which are responsible for exploration of the search space, while back-propagation is used for an effective local search on the best solution obtained by ICA. Experiment results confirm our proposed algorithm to be highly competitive compared to other recently reported methods