Texture Segmentation using Ant Tree Clustering
Channa, A., Rajpoot, N.M. and Rajpoot, K. (2006) Texture Segmentation using Ant Tree Clustering. In: IEEE International Conference on Engineering of Intelligent Systems (ICEIS 2006), 14-15 January, 2006, Islamabad, Pakistan.
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Official URL: http://doi.ieeecomputersociety.org/10.1109/ICEIS.2...
Motivated by the self-assembling behavior of real ants, we present a novel algorithm for texture segmentation which is based on ant tree clustering of wavelet features. In a pattern recognition setting, wavelet features are extracted using either of the two subband filtering methods: discrete wavelet transform (DWT) or discrete wavelet packet transform (DWPT). The feature classification process is inspired by the self-assembling behavior observed in real ants where ants progressively become attached to an existing support and then successively to other attached ants thus building trees based on the similarity of feature vectors. The results thus obtained compare favorably to those of other recently published filtering based texture segmentation algorithms.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||combi texture|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Faculty of Science > Computer Science|
|Depositing User:||Jason Nurse|
|Date Deposited:||18 Dec 2010 12:29|
|Last Modified:||26 Jul 2011 11:17|
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