COLOR TEXTURE SEGMENTATION USING FEATURE DISTRIBUTIONS

Hexagonal-HSV

Cellular decomposition (S,H)={ (0.665, 49.1), (0.665, 169.1), (0.665, 229.1), (0.665,289.1), and (0.665, 349.1) }

Cellular decomposition V component is divided into 3 parts with three interval s (0, 0.45), (0.45, 0.75) and (0.75, 1). Thus, the 7 (S, H) pairs and the 3 V values can be combined into 21 initial colors.

Cluster ＊ The Euclidean distances between each pixel and the 21 respective initial color centers are then computed. ＊ Each pixel can then be assigned to one of the 21 initial color clusters whose center has the smallest color distance to the pixel. ＊ The cluster with the largest error is then chosen to perform further splitting

? This process can be iterated level by level. The repetitive splitting will be stopped when the splitting level reaches 4 or the number of used colors is more than 192. Quantized image

Cluster *the nearest pair of color clusters will be merged until the distance between any two colors are greater than 26. *PSNR value of the quantizatzed image is larger than 30 峰值訊號雜訊比 (peak signal to noise ratio: PSNR) *merged until the number of used colors reaches 50

DEFINITION FEATURE Color histogram ： distribution of color in a texture Local_edge_pattern histogram ： distribution fo local edge patterns in a texture region

Color histogram hc the color histogram hc for a texture region R using the following equation. where n is the number of pixels with color label i, N is the total number of pixels of the texture region.

Local edge pattern descriptors

Local edge pattern (LEP) he for a texture region R can be derived using the following equation where n is the number of pixels with LEP value i, N is the total number of pixels ofthe texture region.

Homogeneity Measure(color) the more the Hc value approaches to 1 the more alike the color histograms hc and hc

Homogeneity Measure(texture) the more the He value approaches to 1 the more alike the color histograms he and h e

Homogeneity Measure

Segmentation method

Hierarchical splitting a block is split to four subblocks,six pairwise H distance as specified in Eq. (6) between color and LEP histograms of the four subblocks. choose a small value The threshold X was experimentally set to value 1.1.

Agglomerative merging p is the number of pixels in the smaller of the two regions and H is the histogram distance measure as specified in Eq.(7) respective color and LEP histograms are summed to the new image region

Agglomerative merging MImin is the smallest merger importance value of all preceding mergers, MImax is the largest merger importance value of all preceding mergers MIcur is the merger importance for the current best merge.

Pixelwise classification discrete disk with radius r and compute the corresponding color and LEP histograms over the disc v is the number of pixels belonging to the ith region and in the 4-neighboors of the examined point.

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