An alternative approach for unsupervised cluster based image retrieval


In this research, we proposed an alternative method to retrieve images from its clusters which is determined by its color histogram. In other approach, it is called cluster based image retrieval model. One common problem with conventional content based image retrieval (CBIR) system is that the output data contains too many non-relevant feedbacks due to the indistinct symmetry of semantic gap. In this paper, two clustering techniques are performed to solve the problem mentioned above; including the misconception of semantic gap, called k-means and hierarchical clustering technique. The proposed method shows a promising retrieval procedure in terms of runtime errors reduction and accuracy. The experimental results determine that this alternative image retrieval model does not only retrieve images to the homogeneity of its features but it is also practically sufficient.

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@inproceedings{Pattanasethanon2012AnAA, title={An alternative approach for unsupervised cluster based image retrieval}, author={Petcharat Pattanasethanon and Boonwat Attachoo}, year={2012} }