An Approach to Image Segmentation Using K-Means Clustering Algorithm
Student: Ayomide Emmanuel Olatunde (Project, 2025)
Department of Computer Engineering
Osun State Polytechnic, Iree, Osun State
Abstract
This project proposes a color-based segmentation method that uses K-means clustering technique. The k-means algorithm is an iterative technique used to partition an image into k clusters. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished. Then the clustered blocks are merged to a specific number of regions. This approach thus provides a feasible new solution for image segmentation which may be helpful in image retrieval.
Keywords
For the full publication, please contact the author directly at: ayomideolatunde3@gmail.com
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Institutions
- Landmark University, Omu-Aran, Kwara State 1
- Lead City University, Ibadan, Oyo State 1
- Lens Polytechnic, offa, Kwara State. 215
- Madonna University, Elele, Rivers State 20
- Madonna University, Okija, Anambra State 2
- Mcpherson University, Seriki Sotayo, Ogun State 1
- Michael and Cecilia Ibru University, Owhrode, Delta State 1
- Michael Okpara University of Agriculture, Umudike 43
- Michael Otedola Col of Primary Educ. Epe, Lagos (affl To University of Ibadan) 8
- Modibbo Adama University, Yola, Adamawa State 15