Automated Digital Image Clustering Algorithm Based on Colour Distance and IDBSCAN
J. Margaret Sangeetha1, P. Velmani2, T.C. Rajakumar3
1J. Margaret Sangeetha*, Research Scholar, Dept. of Computer Science, Manonmaniam Sundaranar University, Tirunelveli, India
2P. Velmani, Asst. Professor, Department of Computer Science, The M.D.T. Hindu College, Tirunelveli, India
3T.C. Rajakumar, Asso. Professor, Department of Computer Science, St. Xavier’s College, Tirunelveli, India
Manuscript received on November 12, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 2717-2722 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7078129219/2019©BEIESP | DOI: 10.35940/ijitee.B7078.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data clustering is inevitable for crucial data analytic based applications. Though data clustering algorithms are capacious in the literature, there is always a room for efficient data clustering algorithms. This is due to the uncontrollable growth of data and its utilization. The data clustering may consider any of the data formats such as text, images, audio, video and so on. Due to the increasing utilization trend of digital images, this work intends to present a data clustering algorithm for digital images, which is based colour distance and Improvised DBSCAN (IDBSCAN) algorithm. The proposed IDBSCAN completely weeds out the annoying process of setting the initial parameters such as 𝜺 and 𝒎𝒊𝒏𝒑𝒕𝒔 by setting them automatically. The performance of the proposed work is analysed in terms of clustering accuracy, precision, recall, F-measure and time consumption rates. The proposed work outperforms the existing approaches with reasonable time consumption.
Keywords: Digital Image, Clustering, DBSCAN, Colour Distance.
Scope of the Article: Clustering