Image Clustering using K-Means on Marine Products
Dr. A. Anushya, Department of Computer Applications, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 19, 2020. | Manuscript published on February 10, 2020. | PP: 280-282 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1369029420/2020©BEIESP | DOI: 10.35940/ijitee.D1369.029420
Open Access | Ethics and Policies | Cite | Mendeley
© 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: In this study, the researcher collected 360 marine product images consist of red snapper, prawn, silver belly, pomfret, mackerel, cuttle fish, lobster, crab and sardine to conduct try-outs at first. Secondly, images are separated from background for processing. Then from the images features are extracted via Gray Level Concurrence Matrix. Finally images are clustered according to its groupings by K-Means clustering algorithm. Since marine products are consumed by most of populaces regularly because of its health benefits, availability of nutrients and low cost. For that reason all and sundry can buying. This research helps to identify them by their physical appearances. Marine products have eye-catching altered physiognomies which are cherished to extricate and conclude a specific category. These physical appearances comprise of size, shape, texture, and color. This research succeeds 83% accuracy for bunch the images into nine clusters.
Keywords: Image Clustering, Gray Level Concurrence Matrix, K-Means
Scope of the Article: Clustering