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Fast and Efficient K-means based Algorithm to Content-based Image Clustering

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Basel Hafiz, Mohamad Mousa, Mohamad Waheed

Basel Hafiz, Mohamad Mousa and Mohamad Waheed. Fast and Efficient K-means based Algorithm to Content-based Image Clustering. International Journal of Computer Applications 152(5):8-14, October 2016. BibTeX

	author = {Basel Hafiz and Mohamad Mousa and Mohamad Waheed},
	title = {Fast and Efficient K-means based Algorithm to Content-based Image Clustering},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2016},
	volume = {152},
	number = {5},
	month = {Oct},
	year = {2016},
	issn = {0975-8887},
	pages = {8-14},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2016910715},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Some of the present approaches compare the user’s query image against all of the database images; as a result, the computational complexity and search space will boost, respectively. The fundamental purpose of the research presented in the present paper is to evolve a general purpose clustering method that can efficiently and effectively handle large capacity image databases. It can be fluently embedded into distinct CBIR systems. In this paper, we developed a novel content-based image clustering technique based on an effective k-means based algorithm. The co-occurrence matrix features and color moments are utilized to evolve an effective and innovative image clustering framework. The texture and color features are integrated to enhancing results obtained using individual descriptors. The introduced k-means based clustering algorithm has been proposed as a preprocessing procedure to accelerate image retrieval and to enhance image retrieval accuracy. The experimental outcomes based on COREL images have been investigated and indicated considerable refinement in terms of quality of image clustering, retrieval accuracy, and speed compared against the conventional k-means method.


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Content-based image clustering, Feature extraction, K-means clustering