Abstract—Efficient and accurate image segmentation is an
important task in computer vision and object recognition. Since
fully automatic image segmentation is hard to handle with
natural images and texture images with complex background,
thus interactive scheme with a few simple user inputs is a very
good addition to image segmentation. For the purpose to
accurately extract objects from different images, this paper
presents a color histogram and Contourlet transform based
interactive image segmentation. In the initialization stage, a
superpixel based initial segmentation is applied to the original
image. After that, the original image will be divided into a
certain number of superpixels. Then, each superpixel will be
represented by a novel superpixel feature based on color
histogram and Contourlet transform. Finally, by using
user-defined strokes, we merge the superpixels which are
similar to marked object superpixels and merge the superpixels
similar to marked background superpixels, until classify all
superpixels. The computational complexity is analyzed, and
comparative experimental results show that the proposed
scheme can reliably and rapidly extract the desired object from
the complex background.
Index Terms—Interactive segmentation, color histogram,
superpixel, Contourlet transform, region merging.
All authors were with the Department of Computer and Information
Science, University of Macau, Macau, China (e-mail: yb27405@umac.mo,
cmpun@umac.mo).
[PDF]
Cite:Guoheng Huang and Chi-Man Pun, "Robust Interactive Segmentation Using Color Histogram and Contourlet Transform," International Journal of Computer Theory and Engineering vol. 7, no. 6, pp. 489-494, 2015.
