In this paper an evaluation of the influence of luminance L* at the L*a*b* color space during color segmentation is presented. A comparative study is made between the behavior of segmentation in color images using only the Euclidean metric of a* and b* and an adaptive color similarity function defined as a product of Gaussian functions in a modified HSI color space. For the evaluation synthetic images were particularly designed to accurately assess the performance of the color segmentation. The testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. From the results is obtained that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume.
Image segmentation consists of partitioning an entire image into different regions, which are similar in some predefined manner. It is an important and difficult task in image analysis and processing. All subsequent steps, such as object recognition depend on the quality of segmentation [
For some time the development of segmentation algorithms attracted remarkable consideration compared with the relatively fewer efforts on their evaluation and characterization [
Perceptual uniform color spaces such as L*a*b*, with the Euclidean metric to quantify color distances are commonly used in color image segmentation of natural scenes using histogram based or clustering techniques among others [
To evaluate the segmentation performance of the Euclidian metric in the L*a*b color space we designed a system that generated synthetic color images, with its associated ground truth (GT), and evaluated the results with Receiver operating characteristics (ROC) curves [
The first comprehensive survey on evaluation methods of image segmentation is presented in [
In [
In [
Comparative tests between an adaptive color similarity function [
In the case of the L*a*b* color space, the RGB image was previously transformed to L*a*b* color space discarding in all cases the luminance L* in order to calculate the Euclidean distance on the planes a*b* (color information) independently of the illumination.
Then the centroid (average of the values a* and b*) representing the colors of the figure and the background in the color space L*a*b* was calculated. Details are shown in [
1) Samples of both background and figure were taken, from which centroid and standard color dispersion were calculated. Details can be consulted in [
2) The 24-bit RGB image (true color) was transformed to a modified HSI color space.
3) For each pixel, the similarity function to the centroids of figure and background was calculated creating two color similarity images (CSI) [
4) Each pixel of the RGB image was classified by calculating the maximum value for each pixel position between the CSI images of the figure and that of the background.
The base shape of the synthetic test image was created with the following features:
1) Concave and convex sections in order to make it more representative of real images, such as natural flowers. 2) Extreme omnidirectional curvature in the entire image to hinder obtaining the edges applying mask edge detectors. 3) The object was centered in the image.
The resulting flower-shaped object in the image is considered as the object of interest and as the ground truth GT in all subsequent tests (
In addition to this object of interest, several features were imposed in order to hinder its color-based segmentation:
1) Low contrast. The contrast between the object and the background in all images was very low for an observer, including some in which at a first glance the user cannot see the difference (e.g. Flower 5 in
The difference between the color characteristics of the object of interest and the background is called Delta by us and occurs at different directions of the HSI space. The tests were performed in color quadrants 0, 60, 120, 180, 240 and 300 degrees.
2) Blurred edges with an average filter. A mean filter of size 3 × 3 pixels was applied to the whole image in order to blur the corners and to make detection of the object more difficult; this was done before the introduction of Gaussian noise.
3) Introduction of Gaussian noise with SNR value = 1 (
The basic colors selected for both figure and background were based on maintaining constant intensity to 0.5 and saturation to 0.3 and only varying the hue. Hue was selected as the parameter because its change integrates the three RGB color channels together, making it more difficult to be processed by extending grayscale techniques to each color channel, thus forcing the segmentation algorithms in evaluation to use the color information holistically.
Samples of pixels corresponding to the figure were obtained by two squares of 2 × 2 pixels starting at the pixel (84, 84) and (150, 150). Samples for background pixels were obtained by two squares of 2 × 2 pixels starting at pixel (15, 15) and (150, 180).
The images were generated in the sectors 0, 60, 120, 180, 240 and 300 degrees corresponding to the images flower_0, flower_1 … flower_5 (
A shadow fading was applied to all noisy blurred images with the light center in the fixed coordinates (150, 150) in images of 256 × 256 pixels. It was applied gradually with 10% increments in each step.
In this section we show the results of TP (true positives) and FP (false positives) plotted against the level of shadow fading, representing each 10% step of increment. The first position means no shadow and position 11 means 100% shadow fading. All the images had the same post-processing: elimination of areas smaller than 30 pixels and a morphological closing with a circular structuring element of radius equal to two pixels.
The results of the application with the solution given by [
120˚, 180˚, 240˚ and 300˚) and at 10% increments of the shadow fading.
As it is shown in the graphs of
We can see three general trends in the FP behavior in
To obtain a representative ROC curve illustrating behavior of the Euclidean metric in the L*a*b* space (rejecting L*) compared to the color similarity function [
In the ROC curve corresponding to the average of TP and FP of all flowers, it can be seen that the results of the adaptive similarity function are maintained in the high efficiency area (coordinate 0, 1) while the color
Flower | Line Color | Euclidean metric in L*a*b* rejecting L* | Color similarity function [ |
---|---|---|---|
0 | Blue | 60% (position 7) Increases at 45˚ | Immune |
1 | Green | 30% (position 4) Increases slowly and progressively | Immune |
2 | Red | 40% (position 5) Sharply increases | Immune |
3 | Cyan | 70% (position 8) Increases at 45˚ | Immune |
4 | Purple | 20% (position 3) Increases slowly and progressively | Immune |
5 | Yellow | 50% (position 6) Sharply increases | Immune |
segmentation in L*a*b* space progressively moves away from the high efficiency area.
The L*a*b* results keep stable initially and later slowly and progressively moves to the upper right area of the ROC curve that can be thought of as the “liberal” side (coordinate 1, 1) as they make positive classifications, and, although there is weak evidence that almost all positives were classified properly, they have a high rate of false positives.
Regarding the evaluation of the color segmentation method with really difficult conditions, we can notice that the adaptive color similarity function performed well in all tests and remained close to the high efficiency zone of the ROC curves (coordinates 0, 1) without noticeable changes when the level of faded shadow increases as shown in the corresponding PLOT curves.
The segmentation algorithm using the L*a*b* color space and discarding L* in calculating the Euclidean distance, suffered errors in all cases. It manifested in different degrees and at different levels of faded shadow (20% to 80%). Three types of trends or recurring symmetries can be noticed in sectors with 180 degrees of difference: 1) Rise of the curve gradually (Flowers 1 and 4); 2) Rise abruptly (Flowers 2 and 5), and 3) Increase near at 45˚ angle (Flowers 0 and 3).
As it can be seen from the results of both direct segmentation, and from PLOT & ROC curves, that the adaptive color similarity function in all cases exceeded the Euclidean distance in color space L*a*b* and discarding L*. The similarity function segmentation method performed well in all cases with rates higher than 95% of true positives (TP) and false positive (FP) rate less than 3% on average.
According to the experiment results we believe that keeping high values of TP (true positive) increased only from the FP (false positive) is due to the position of the center of the shadow fading in (150, 150). If this position is moved away from the object of interest, we can reduce the quantity of TP.
For future work we wish to evaluate different color zones like with different saturations, gray images, and with delta saturation among others. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. Instead of exchanging color spaces in the experiments, it would only be necessary to exchange the metric or the similarity function.
It can be noticed that the non-consideration of the luminance parameter L* in calculating Euclidean distance (in each pixel of the object or of the background) did not made it immune to changes in lighting; so simple shadow can alter the quality of the results, concluding from them that the parameters a*b* from the color space L*a*b* are not independent of the L* parameter as one might suppose.
The authors of this paper wish to thank the Centro de Investigaciones Teóricas, Facultad de Estudios Superiores Cuautitlan (FES-C); Universidad Nacional Autónoma de México (UNAM), México; PAPIIT IN112913 and PIAPIVC06, UNAM; Centro de Investigación en Computación (CIC); Secretaría de Investigación y Posgrado (SIP); Instituto Politécnico Nacional (IPN), México, and CONACyT, México, for their economic support to this work.
Rodolfo Alvarado-Cervantes,Edgardo M. Felipe-Riveron,Vladislav Khartchenko,Oleksiy Pogrebnyak, (2016) A Study on the Influence of Luminance L* in the L*a*b* Color Space during Color Segmentation. Journal of Computer and Communications,04,28-34. doi: 10.4236/jcc.2016.43005