color model mixtures, are explored. It was observed that two of the studied models, YCbCr and linear, were more efficient for the purpose of image segmentation.
Optimizing Image Segmentation Using Color Model Mixtures Aristide Chikando, Jason Kinser School of Computational Sciences, George Mason University, Manassas, VA, USA (achikand, jkinser)@gmu.edu
Abstract Several mathematical color models have been proposed to segment images based on their color information content. The most frequently used color models of such sort include RGB, HSV, YCbCr, etc. These models were designed to represent color and in some cases emulate how the reflection of light on a given entity is perceived by the human eye. They were, however, not designed specifically for the purpose of image segmentation. In this study, the efficiency of several color models for the application of image segmentation is assessed and more efficient color models, consisting of color model mixtures, are explored. It was observed that two of the studied models, YCbCr and linear, were more efficient for the purpose of image segmentation. Additionally, by employing multivariate analysis, it was observed that the model mixtures were more efficient than the most commonly used models studied, and thus optimized the segmentation.
1. Introduction The increasing use of digital equipment in scientific research has made image segmentation, the process of distinguishing images captured by these equipments from the background or from each others, an intrinsic part of data analysis. During the segmentation process, the image content is extracted and differentiated based on characteristics such as hue, intensity and texture. Recently, several models have been proposed that require the segmentation of color images. [1] [2] [3] These images are photographs in which the color information for each pixel is originally in an RGB format. While RGB is sufficient for displaying information, its lack of proper separation of hue information from intensity information renders it horrendous for the purpose
of segmentation. Thus, these models convert RGB information to one of a variety of other color formats in order to separate intensity information from hue information. It should be noted that these models do not rely solely on segmentation by color but it is an integral part of the process. The importance of color to the segmentation process is illustrated by the fact that intensity is more a function of illumination gradients than it is of the object whereas, the hue, that is encoded by the color, tends to be dependent on qualities of the object. Thus, hue is a better discriminant. There exist several color formats that separate intensity and hue, but these were designed for purposes other than image segmentation. In this study several images are considered. In each image regions of interest (ROIs) are manually selected. Each ROI embraces a single object and within an image no two ROIs embrace the same object. Each image is converted to a color format and the statistics of each ROI is calculated. The color format that best separates the ROIs statistically will be considered as the color format that best segments the image. The next section explains the study in more detail, the third section briefly reviews the color formats used in this study, and the results are provided in the fourth section.
2. Experimental Design The experimental data consists of images ^I n : n 1...N ` where N is the number of images, and each image is an RGB representation of a photograph. These photographs are from the Arizona State University database [4] and have not been altered. The images are of outdoor scenes which contain trees, meadows, sky, snow, etc. Shape information is of minimal importance since objects such as these do not have a defined
Proceedings of the 34th Applied Imagery and Pattern Recognition Workshop (AIPR05) 0-7695-2479-6/05 $20.00 © 2005
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shape. So, for these images color/texture is the best discriminating feature. Rectangular segments within each image were manually selected. These segments (or ROIs) contain a single object and no two segments within a single image contained the same object. Thus, a format that best isolates these ROIs also is best for image segmentation. However, the segments may be complicated. For example, a segment may be the foliage of a tree which contains an irregular texture but still represents a single concept or object. The segments for image In are denoted as G n , m : m 1...M n
^
total number of color formats used in the study. Thus, : k G n , m represents the m-th segment
denoted by a superscript, example,
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
: ky ^Gn,m `. For
: HHSV ^Gn,m ` represents the H channel
from the HSV color format. The fact that each segment embraces a unique object provides means for isolating and comparing color channels performances on these objects. The intent is that a distribution of pixels in one segment (as represented in a single channel) should not significantly overlap the distribution of another segment. The Gaussian distribution of the pixels from
: ky ^Gn,m ` is
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H k y m 2 n . If the y-th channel of the k-th color format efficiently segments the image then H k y m1 n and H k y m 2 n should have very little overlap. Figure 1 shows the distribution of the RGB red channel from two different ROIs and figure 2 displays the distributions for the same regions using the H channel from the HSV format.
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than the model : RGB although this model is far from ideal. It is a better representation for the purpose of segmentation (specifically for these two ROIs in this single image). Channels from different color formats do not have the same range of value. In the RGB format for example, the red channel values range from 0 to 256, whereas in the HSV format, the H channel values range from 0 to 1. Because this study compares channel values from different color formats, scaling is done in order for all channels to have the same range. Subsequently, all channels are methodically scaled to range from 0 to 256. After the distributions are scaled the overlap is measured by,
C k y m1 m 2 n
¦H
k y m1 n
[ x] H k y m 2 n [ x] . (1)
x
A lower value of C indicates a better segmentation for two ROIs of a single channel. Since an image contains several segments a scoring matrix is used to contain all of the possible pairings of segments,
Dk y n [ p, q ]
Ck y p q n
;p q.
(2)
Since D[p,q] = D[q,p] the condition p
