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Abstract—In order to accurately extract various color regions in a color image, a multi-color extraction method based on the semantic color is put forward in this paper. By introducing semantic color and establishing semantic color model, the method can rationally solve the color extraction problem. This method makes use of fuzzy clustering algorithm which distributes pixels of an image into each color area, and achieves the extraction of an object in a color image which is composed of multiple colors. Compared with the Fuzzy c-means (FCM) clustering segmentation algorithm, the experiment results demonstrate the effectiveness of the method. The multi-color extraction method can effectively solve the extraction of multiple colors in a color image with complicated features.

I. INTRODUCTION

MAGE segmentation is critical to image processing, computer vision and pattern recognition, it occupies an important position in the image project. From the

segmentation results, it is possible to identify regions of interest and subimages (which represent objects) in the scene, which is very beneficial to the subsequent image analysis or processing. Very important information can be obtained from effective image segmentation for further analysis,recognization, track and compression coding of image. At present, the image segmentation still didn't get well settled.Because of the particularity of the image itself and complexity, especially for specific applications for different needs, there is not a recognized standard to weight the segmentation quality. Color image processing is being paid more and more attention due to color image providing more information than monochrome image. Several segmentation approaches based on clustering have been developed [1]; fuzzy clustering-based segmentation is our concern here. Since people were involved in the image processing, so the complexity of image itself and visual characteristic of people must be considered. As an image itself has much uncertainty and inaccuracy, namely, it is fuzzy. L. A. Zadeh proposes to use fuzzy set theory to study the uncertainty and inaccuracy [2], which provides an effective method for the intelligent information processing, it turned out to be very appropriate to process image with fuzzy clustering. Color image segmentation faces the other problem is the selection of

Manuscript received July 1. 2011.This work was supported by the National Natural Science Fund (61062005) and key laboratory of network communication and intelligent computation.

X. F. Wang, H. W. Ding and B. L. Li are with the School of Information Science and Engineering, Yunnan University, Kunming 650091 China (e-mail: xiaofengwang99@163.com ).

X. L. Shi and J. H. Chen are with the School of Information Science and Engineering, Yunnan University, Kunming, China. Phone: 871-503-1301; fax: 871-503-1301; e-mail: xlshi@ynu.edu.cn (X. L. Shi, corresponding author).

J. H. Zhang is with the associate Professor of the School of Information Science and Engineering, Yunnan University, Kunming, China (e-mail: jhzhang_2006@yahoo.com.cn).

appropriate color space. Because of the close relationship between color and semantics [3], we propose to apply semantic color model to extract color in this paper, and asemantic color database is established, so here puts forward a multi-color extraction method based on the semantic color.We use fuzzy clustering algorithm to make distributing color pixels of an image into the various color districts, and extract all colors which compose the object.

II. MATERIAL AND METHOD

A. Semantic Color As an important feature and content of image, color is the

main visual clue for image processing, analysis and retrieval, and it has the power of arousing emotion [3]. A crucial question is how to describe the semantic concept through a appropriate structure. In the process of image processing, methods are different according to different purposes of application to express color. The method of numerical valuewith RGB color is widely employed to express image features [4]. In RGB space the three-primary colors (red, green, blue) are additive, as shown in Fig.1, all sorts of colors are scaled through the compound of the three primary colors. The boundary between adjacent colors is similar but its RGB values are distinct. The RGB color model is well matched with the fact that human eyes can strongly feel three-primary colors, unfortunately, it can't well adapt to the colors fromactual people's explanations. It does not accord with human visual characteristic, although it will be used in this paper.

A Multi-Color Extraction Method Based on Semantic Color Model Xiao Feng Wang, Xin Ling Shi, Jian Hua Chen, Jun Hua Zhang, Hua Wei Ding, and Bao Lei Li

I

Fig.1. Color composition principle

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Actually, when viewing a color image, the perception and description of colors from people are fuzzy and subjective, which are based on semantic description not accurate RGB value. So it determines the limits of RGB color in the image segmentation, but the semantic description can solve the problem. Semantic representation methods need to identify and explain color, and often need to use human knowledge reasoning. It is approximate to human's thinking reflection. The color is a kind of low-level feature in image processing and contains a large amount of semantic information. Therefore, people usually made a semantic analysis of colors through defining semantic colors which refer to the colors that can make a distinction between different objects. The semantic color was applied in video [5] and blood cell images [6], so we can appoint semantic colors in image processing. Because of the diversity of colors and the uncertainty of processed objects, we can define plentiful semantic colors. Color structure based on semantic description can be presented with the following figure, as shown as Fig.2. We take the six vertexes and center as the basic color classes and each class has many semantic colors [7].

In a color image, the semantic description of a color is a color range which has a lot of RGB values of colors, so we can conclude that a semantic color is a color district. Image should be divided into different color areas before extracting color. The fuzzy clustering method is adopted to proceed color clustering and achieve the division of color district according to semantic describes as a color area which can not be specified by a RGB value. Because of the close relationship between the image color and image semantic,semantic colors can be extracted based on color image segmentation and defined color semantic vocabularies and asemantic color model is built in this paper.

B. Semantic Color Model (SCM) The RGB color model is shown as in the Fig.3. If we

project along the diagonal of the RGB cube from the white vertex to the black origin, we can get a hexagon shape of the cube. Six vertex of RBG cube are red (R), yellow (Y), green (G), and cyan (C), blue (B), magenta (M) which in turn constitute hexagon shape six basic color points, and white (W) is taken as a hexagonal central point. So we can fill the hexagon with commonly defined semantic colors. The plane of hexagon can indicate affluent semantic colors, which are

gradient and correlative to one another. The correlation of two colors is expressed with similarity each other. Similarity refers to the degree of similarity between two pixels of image, which is weighted by the max-min distance of them. The max-min distance method will be discussed below in the fuzzy clustering algorithm.

The hexagonal central point is white and every vertex onthe surface is the same distance from the center. The colors from white to each vertex color is changed along with the hue,saturation and brightness of color, which is gradually changed and adjacent colors is similar, so we can arrange the commonly used semantic colors with maximal partition tree in the hexagon. For example, in the big category of blue, there are more than 19 kinds of specific semantic colors which include aqua blue, power blue, light blue, azure, grey blue, dark blue, sapphire blue, bronze blue, azurite and so on [7].We expound the blue class and take these colors for example below.

Number the nine colors consecutively as C1 (aqua blue), C2 (power blue), C3 (light blue), C4 (azure), C5 (grey blue), C5 (dark blue), C7 (sapphire blue), C8 (bronze blue) and C9(azurite). The original data matrix can found by the RGB value of the above nine colors.

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ijx (1)

The similarity-relation matrix 99)( ijrR is found by the max-min formula as follows:

Fig.2. Semantic color sample

Fig.3. RGB color model

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000.1732.0742.0479.0458.0483.0393.0356.0342.0732.0000.1564.0364.0348.0367.0299.0270.0261.0742.0564.0000.1645.0617.0651.0530.0479.0462.0479.0364.0645.0000.1858.0883.0821.0742.0715.0458.0348.0617.0858.0000.1771.0858.0776.0748.0483.0367.0651.0883.0771.0000.1814.0737.0709.0393.0299.0530.0821.0858.0814.0000.1904.0871.0356.0270.0479.0742.0776.0737.0904.0000.1963.0342.0261.0462.0715.0748.0709.0871.0963.0000.1

R (2)

Based on the similarity-relation matrix R, choose C2 as the vertex , a maximal partition tree was yielded, as shown in Fig.4, where nine colors were denoted by nine points, and they were linked by the branches based on the similarity-relation matrix R.

Utilizing this method, the common semantic colors can be ranked in the hexagon, so we can build a semantic-based color model, as shown in Fig.5. Based on the SCM, asemantic color database is established in the experiment, the database includes more than 200 kinds of common Chinese traditional colors [7].

C. FMM Clustering Algorithm As one of the fuzzy clustering algorithms, the fuzzy

max-min clustering algorithm (FMM) based the equivalence relations has certain superiority. It is a kind of fuzzy clustering analysis method based on fuzzy equivalence

relation and constructs the similarity of the classification objects which is expressed by max-min formula method [8].In fuzzy clustering, a data element can belong to more than one cluster, each element has a membership relative to a clustering center, and each cluster is a fuzzy set of a series of elements. The similarities of an element are different from center to center. Fuzzy clustering is a process of assigning these memberships, and then using them to assign data elements to one or more clusters. The fuzzy clustering analyzing in this study was carried as the following steps:

Step 1: Normalize the original image data

),.....,,,( 321 nxxxxX . Step 2: Determine the amount k (0< k < n ) of colors of

object and select a color as the clustering center kvaccording to the established semantic color model.

Step 3: Traverse every pixel, calculated the similarity ijr

with cluster center kv , the max-min formula as follows:

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m

kjkik

m

kjkikij xxxxr

11),(),( (3)

Step 4: Found the similarity-relation matrix )( ijrR .

Step 5: Choose kx as the vertex, obtain the maximal partiton

tree.

Step 6: Set value of threshold � , and cluster analysis accord

to the similarity-relation matrix R . Step 7: If an ideal extraction result is achieved, then extract

second color and repeat from step 2 to 5 until all colorextracted, or reset threshold � return to step 5.

D. Multi-color Extraction of Color Image Based on SCM In our experiment, the process of color extraction is

shown as in Fig.6. We employ the Semantic Color Model (SCM) which is based on RGB color image to determine one of target colors as the first center, and then we use FMM traverse every pixel since it is the three-dimensional vector ofan image which include lots color information. Therefore, we can get memberships of every pixel relative to the center. We set a value of threshold which is compared with the membership, if a membership is bigger than the threshold; weretain the corresponding pixel, or set the pixel to white. And we can estimate the result of extraction, if it is satisfactory, westop to extract the second color of the object, otherwise the threshold will be reset. So we can extract multiple colors which constitute all color components of the target. Of coursethe operation of multiple colors is serial. Compared with FCM method [9], we use FMM for extracting since color it costs less time without iteration. The result of multi-color extraction of color image using FMM segmentation will be discussed in the next section.

Fig.4

.

Maximal partition tree

Fig.5. Semantic color model

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III. RESULTS AND DISCUSSION In this section, we investigate the very applicability of the

above FMM in color image segmentation. Our experimental results here demonstrate that when the segmentation for color images is required, the above FMM has obvious advantages over FCM.

In the experiment results, Fig.7 (a) and Fig.8 (a) show an original color image. Fig.7 (b) and Fig.8 (b) show the corresponding segmentation results using FCM, respectively c=3. Fig.7 (c) and Fig.8 (c) show the segmentation results using the proposed FMM. Fig.7 (d) and Fig.8 (d) show the results of double colors which compose the object. In the Fig.7 (b) and Fig.8 (b), the segmented object is of single feature which has the same RGB value, but the Fig.7 (c) and Fig.8 (c) retain the color information and the special distribution of the pixels. By analyzing the segmentation results, we can conclude that Fig.7 (d) has more accuracy color information than Fig.7 (b). Obviously, the segmentation results of the proposed FMM are better than comparable with those of FCM, from the perceptual viewpoint.

IV. CONCLUSION

In this paper, a novel multi-color extraction method for color images is presented to accurately extract various color regions. The aim of clustering is to decrease the data volume through generalizing the similar data. At present, common cluster algorithms, such as FCM, usually need to be given a number of clustering centers in order to classify an image, so it is difficult to achieve for any given dataset. But in this paper, according to the established SCM, the FMM based on the fuzzy cluster algorithm is easy to distribute the cluster center. It is very good targeted for color extraction. So the problem of uncertainty of clustering center is overcome. And the result of image extraction is also closer to real image. Furthermore, compared with the FCM algorithm, the proposed method is more flexible than the FCM, and it improves the processing speed to achieve the ideal effect. However, in the method, the order of color selection will affect the result of extraction to some extent, which will be improved in the future.

Fig.6. Flow chart of multi-color extraction

(a) Original image (b) FCM

(c) Single color (FMM) (d) Multi-color (FMM) Fig.7. (a-d) Segmentation results of both algorithms

(a) Original image (b) FCM

(c) Single color (FMM) (d) Multi-color (FMM) Fig.8. (a -d) Segmentation results of both algorithms

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ACKNOWLEDGMENT

This work was supported by the National Natural Science Fund (61062005) and the key laboratory of network communication and intelligent computation.

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