Title: Quantum inspired automatic image clustering

Biography:

Dr. Siddhartha Bhattacharyya completed PhD from Jadavpur University, India in 2008. He is the recipient of several awards including the South East Asian Regional Computing Confederation (SEARCC) International Digital Award ICT Educator of the Year in 2017. He has been appointed as the ACM Distinguished Speaker for the tenure 2018-2020. He is currently serving as the Principal of RCC Institute of Information Technology, Kolkata, India. He is a co-author of 5 books, co-editor of 30 books and has authored more than 250 research publications. His research interests include soft computing, pattern recognition, multimedia data processing, hybrid intelligence and quantum computing.

Abstract

Clustering refers to the process of grouping of the elements of a dataset based on the similarity of the underlying features. Existing clustering algorithms requires human intervention in the form of having some a priori information regarding the number of clusters to which a dataset is likely to be clustered. However, this mechanism is not a reliable one especially when it comes to real time situations. Hence, determination of the optimal number of clusters from a dataset on the run is a challenging proposition in the computer vision research community. The process of finding the optimal number of clusters in a dataset so as to have a reliable clustering result can therefore be considered as an optimization problem. Several metaheuristic algorithms are efficient in this regard.

This study introduces some novel quantum inspired metaheuristics which are evolved on the basis of quantum mechanical principles to determine the optimal number of clusters from an image dataset. The evolved quantum inspired metaheuristics have been found to be more efficient as compared to their classical counterparts as far as the time complexity and robustness is concerned.