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Color I mage Segmentation Based on Mean Shift and Normalized Cuts

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In this correspondence, we develop a novel approach that

provides effective and robust segmentation of color images. By incor-

porating the advantages of the mean shift (MS) segmentation and the

normalized cut (Ncut) partitioningmethods, the proposedmethod requires

low computational complexity and is therefore very feasible for real-time

image segmentation processing. It preprocesses an image by using the

MS algorithm to form segmented regions that preserve the desirable

discontinuity characteristics of the image. The segmented regions are

then represented by using the graph structures, and the Ncut method

is applied to perform globally optimized clustering. Because the number

of the segmented regions is much smaller than that of the image pixels,

the proposed method allows a low-dimensional image clustering with

significant reduction of the complexity compared to conventional graph-

partitioning methods that are directly applied to the image pixels. In

addition, the image clustering using the segmented regions, instead of the

image pixels, also reduces the sensitivity to noise and results in enhanced

image segmentation performance. Furthermore, to avoid some inappro-

priate partitioning when considering every region as only one graph node,

we develop an improved segmentation strategy using multiple child nodes

for each region. The superiority of the proposed method is examined and

demonstrated through a large number of experiments using color natural

scene images.

for more details on this project :

Contact No : 9677051565

E-mail :candidtutors@gmail.com

Address :

Candid industrial training,

137 B 2nd street shanthi nagar,

Chrompet, Chennai 600048

Filed in: IEEE Projects

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