tailieunhanh - Data Mining and Knowledge Discovery Handbook, 2 Edition part 112

Data Mining and Knowledge Discovery Handbook, 2 Edition part 112. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 1090 Zhongfei Mark Zhang and Ruofei Zhang denote a dilation of the mother wavelet x y by a m where a is the scale parameter and a rotation by O l x AO where AO 2n V is the orientation sampling period V is the number of orientation sampling intervals. In the frequency domain with the following Gabor function as the mother wavelet we use this family of wavelets as our filter bank W u v exp -2n2 a2u2 CT2v2 8 u W exp 2n nX u W 2 o-2v2 22 exp -2 2 2 t where A is a convolution symbol S - is the impulse function Gu 2ncx 1 and G 2nay 1 Gx and Gy are the standard deviations of the filter along the x and y directions respectively. The constant W determines the frequency bandwidth of the filters. Applying the Gabor filter bank to the blocks for every image pixel p q in U the number of scales in the filter bank by V array of responses to the filter bank we only need to retain the magnitudes of the responses Fmlpq Wmipq m 0 . U 1 l 0 .V 1 Hence a texture feature is represented by a vector with each element of the vector corresponding to the energy in a specified scale and orientation sub-band . a Gabor filter. In the implementation a Gabor filter bank of 3 orientations and 2 scales is used for each image in the database resulting in a 6-dimensional feature vector . 6 means for Wmi for the texture representation. After we obtain feature vectors for all blocks we perform normalization on both color and texture features such that the effects of different feature ranges are eliminated. Then a k-means based segmentation algorithm similar to that used in Chen Wang 2002 is applied to clustering the feature vectors into several classes with each class corresponding to one region in the segmented image. Figure gives four examples of the segmentation results of images in the database which show the effectiveness of the segmentation algorithm employed. After the segmentation the edge map is used with the water-filling algorithm Zhou et al. 1999 to describe the shape