Objective

2

Image segmentation is a process of dividing an image into different regions such that each region is homogeneous but the union of any two adjacent regions is not. As the first step in image analysis and pattern recognition

image segmentation serves as a fundamental step in numerous computer vision applications

such as object detection

content-based image retrieval

and medical image analysis. Threshold-based methods

which subdivide the image into several homogenous regions on the basis of pixel intensities

are popular segmentation techniques. Numerous algorithms have been proposed in this direction

which include gray-level thresholding and interactive pixel classification. Among these algorithms

the frequently used maximum fuzzy correlations are widely adopted to measure the appropriateness of fuzzy two partitions for the image segmentation purpose due to the unavoidable ambiguities

fuzziness

and uncertainty of the image information. However

this method has some limitations

i.e.

the partition number needs to be preset

the results have isolated noise

and maximum fuzzy correlation approach cannot be extended to color image segmentation.

Method

2

Most existing gray-level image segmentation techniques could be extended to color image. They can be directly applied to each component of a color space

and then

the result can be combined in a certain way to obtain the final segmentation result. However

one of the problems is how to use the color information as a whole for each pixel and how to select the color representation for segmentation because each color representation has advantages and disadvantages. To address these problems

an unsupervised hierarchical color image segmentation through maximum fuzzy correlation and graph cut is proposed. First

we oversegment the color image into superpixels to improve the efficiency of hierarchical image segmentation. Then

we combine the fast fuzzy correlation with graph cut to form a bi-level segmentation operator

which can suppress the isolated noise caused by the single threshold-based approach and enforce the spatial coherence in the thresholding segmentation approach. Here

an iterative calculation scheme is presented to reduce redundant computations in fuzzy correlation evaluation. Finally

a top-down hierarchical segmentation approach has been designed. By iteratively performing this bi-level segmentation operator

multilevel image segmentation is achieved in a hierarchical manner. Starting from the input color image

our algorithm first selects the color channel that can best segment the image into two labels and then iteratively selects channels to further split each label until convergence. In practice

we partition the 3D color space and adopt the idea of k-d tree to record the segmentation process.

Result

2

The presented hierarchical segmentation is implemented in Matlab 7.0. Quantitative and qualitative evaluations are performed to compare with those from the state-of-the-art methods. To test the effectiveness of graph cut on the basis of two fuzzy correlation partitions

we compare our method with maximum fuzzy correlation. The experiment shows that our method can overcome the isolated noise and obtain satisfactory results. To demonstrate the segmentation performance of our algorithm on the color images

the Berkeley segmentation database is used

which consists of 300 natural images of diverse scene categories. We quantitatively compare the performance of our method with existing SAS

Ncut

and JSEG methods. Among these methods

SAS and Ncut are semi-supervised hierarchical superpixel-based color image segmentation methods. However

JSEG is an unsupervised color image segmentation method. By utilizing four widely used metrics

we can find that our method outperforms the Ncut and JSEG methods in terms of precision

and the running time is improved by 20%. Compared with SAS

our method obtains subpar results because it uses only pixel color information and is fully unsupervised. In comparison

the SAS approach uses high-level features

such as texture

edges

and color for segmentation.

Conclusion

2

An unsupervised hierarchical image segmentation approach is presented in this paper. The algorithm uses superpixels as segmentation primitives and iteratively partitions all superpixels using a bi-level segmentation operator

which combines fuzzy correlation and graph cut. By using this scheme

the proposed method can effectively handle color images and provide an important reference for the application of the maximum fuzzy correlation algorithm in the field of unsupervised color image segmentation. The limitation of the proposed approach is that it only segments images by using color information

which leads to suboptimal results when the objects and background have similar colors. Utilizing high-level cues in the proposed hierarchical segmentation framework while maintaining it as an unsupervised approach is a non-trivial task. We plan to study this issue as part of our future work.