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This paper introduces a quantum-inspired framework with the bat optimization algorithm for automatic clustering of image datasets. The aim of this work is to find out the optimal number of clusters from an image dataset on a run. A comparison has been produced between the quantum-inspired bat optimization algorithm and its classical counterpart. As a result, it is seen that the quantum-inspired version outperforms its classical counterpart. Computational experiments have been conducted on four Berkeley image datasets.

In automatic image clustering, high homogeneity of each cluster is always desired. The increase in number of thresholds in gray scale image segmentation/clustering poses various challenges. Recent times have witnessed the growing popularity of swarm intelligence based algorithms in the field of image segmentation. The Spider Monkey Optimization (SMO) algorithm is a notable example, which is motivated by the intelligent behavior of the spider monkeys. The SMO is broadly categorized as a fission-fusion social structure based intelligent algorithm. The original version of the algorithm as well as its variants have been successfully used in several optimization problems. The current work proposes a quantum version of SMO algorithm which takes recourse to quantum encoding of its population along with quantum variants of the intrinsic operations. The basic concepts and principles of quantum mechanics allows QMSO to explore the power of computing. In QMSO, qubits designated chromosomes operate to drive the solution toward better convergence incorporating rotation gate in Hilbert hyperspace. A fitness function associated with maximum distance between cluster centers have been introduced. An application of the proposed QSMO algorithm is demonstrated on the determination of automatic clusters from real life images. A comparative study with the performance of the classical SMO shows the efficacy of the proposed QSMO algorithm.

Clustering, a well-known technique, is used to divide a data set into number of groups, called clusters. Differential evolution and particle swarm optimization are robust, fast and very effective search techniques. To increase computational capability, two different quantum inspired meta-heuristics for automatic clustering, have been proposed here. An application of quantum inspired techniques has been demonstrated for automatic clustering of image data sets. These techniques are able to find optimal number of clusters “on the run” for an image data sets. As the comparative research, a comparison has been made between the proposed techniques and their conventional counterparts for four images data set. Effectiveness of the proposed techniques has been exhibited against the fitness value, standard deviation and mean of the fitness, standard error and computational time. Finally, two separate statistical superiority test, referred to as i-test and Friedman test have been performed to prove the superiority the of proposed approaches in their favor.