A hierarchical approach to color image segmentation using homogeneity

By Heng-Da Cheng, Ying Sun. IEEE Transactions on Image Processing, 2000

346 citations recorded by the source corpus.

In this paper, a novel hierarchical approach to color image segmentation is studied. We extend the general idea of a histogram to the homogeneity domain. In the first phase of the segmentation, uniform regions are identified via multilevel thresholding on a homogeneity histogram. While we process the homogeneity histogram, both local and global information is taken into consideration. This is particularly helpful in taking care of small objects and local variation of color images. An efficient peak-finding algorithm is employed to identify the most significant peaks of the histogram. In the second phase, we perform histogram analysis on the color feature hue for each uniform region obtained in the first phase. We successfully remove about 99.7% singularity off the original images by redefining the hue values for the unstable points according to the local information. After the hierarchical segmentation is performed, a region merging process is employed to avoid over-segmentation. CIE(L*a*b*) color space is used to measure the color difference. Experimental results have demonstrated the effectiveness and superiority of the proposed method after an extensive set of color images was tested.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d0ce077f42a9e4aa557db52632c3753a09a843f0

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Citations in the corpus

Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints (2007, neutral): “analysis in [36].”
Identification of broad-leaved dock (Rumex obtusifolius L.) on grassland by means of digital image processing (2006, methodology): “The local homogeneity was calculated according to Cheng and Sun (2000). First, the standard deviation image of I was calculated by”
A Survey of Digital Map Processing Techniques (2014, background): “…spatial sampling rate (or resolution), sampling spot size (or blur), bit depth of the resulting data, brightness and contrast settings, as well as scanner calibration procedures, are determinants of the properties of the resultant image and subsequent extraction techniques heavily depend on…”
Color image segmentation: advances and prospects (2001, neutral): “[70] proposed a hierarchical segmentation which identi"es the uniform region via a thresholding operation on a homogeneity histogram.”
A simple and effective histogram equalization approach to image enhancement (2004, methodology): “where pmax = max{p(x,y) | (x, y) ∈ A} and pmin = min{p(x,y) | (x, y) ∈ A}. 5. Calculate the histogram H( p)based on the values G(x,y) . 6. Since the histogram H( p)is very noisy, we need to remove the noise, and we use the method in [ 27 ] to smooth the histogram.”