17,248 research outputs found
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
Gray Image extraction using Fuzzy Logic
Fuzzy systems concern fundamental methodology to represent and process
uncertainty and imprecision in the linguistic information. The fuzzy systems
that use fuzzy rules to represent the domain knowledge of the problem are known
as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and
subsequent extraction from a noise-affected background, with the help of
various soft computing methods, are relatively new and quite popular due to
various reasons. These methods include various Artificial Neural Network (ANN)
models (primarily supervised in nature), Genetic Algorithm (GA) based
techniques, intensity histogram based methods etc. providing an extraction
solution working in unsupervised mode happens to be even more interesting
problem. Literature suggests that effort in this respect appears to be quite
rudimentary. In the present article, we propose a fuzzy rule guided novel
technique that is functional devoid of any external intervention during
execution. Experimental results suggest that this approach is an efficient one
in comparison to different other techniques extensively addressed in
literature. In order to justify the supremacy of performance of our proposed
technique in respect of its competitors, we take recourse to effective metrics
like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise
Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy
Inference System (FIS), Membership Functions, Membership values,Image coding
and Processing, Soft Computing, Computer Vision Accepted and published in
IEEE. arXiv admin note: text overlap with arXiv:1206.363
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A niching memetic algorithm for simultaneous clustering and feature selection
Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data
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