Foreign Fiber Image Segmentation Based on Maximum Entropy and Genetic Algorithm

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1 Journal of Compuer and Communicaions, 215, 3, 1-7 Published Online November 215 in SciRes. hp:// hp://dx.doi.org/1.4236/jcc Foreign Fiber Image Segmenaion Based on Maximum Enropy and Geneic Algorihm Liping Chen, Xiangyang Chen, Sile Wang, Wenzhu Yang, Sukui Lu School of Compuer Science and Technology, Hebei Universiy, Baoding, China Received Augus 215 Absrac In machine-vision-based sysems for deecing foreign fibers, due o he background of he coon layer has he absolue advanage in he whole image, while he foreign fiber only accoun for a very small par, and wha s more, he brighness and conras of he image are all poor. Using he radiional image segmenaion mehod, he segmenaion resuls are very poor. By adoping he maximum enropy and geneic algorihm, he maximum enropy funcion was used as he finess funcion of geneic algorihm. Through coninuous opimizaion, he opimal segmenaion hreshold is deermined. Experimenal resuls prove ha he image segmenaion of his paper no only fas and accurae, bu also has srong adapabiliy. Keywords Foreign Fibers, Image Segmenaion, Maximum Enropy, Geneic Algorihm 1. Inroducion The foreign fibers in coon refer o hose non-coon fibers and dyed fibers, such as polypropylene fiber silk, hemp, feahers, colored line, colored cloh, hairs and so on. Though very low conen of foreign fibers in coon, he presence of foreign fibers will seriously affec he qualiy of he final coon exile producs, as hey may debase he srengh of he yarn, hey are no easy o be dyed, and his will lead o grea economic loss for he coon exile enerprises [1]. Therefore, fas and accurae measuremen of foreign fibers in lin coon enerprises is an urgen problem. Using machine vision echnology o idenify and measure he foreign fiber is an effecive and feasible soluion. The basis and key of he measuremen of he foreign fiber conen is he foreign fiber s classificaion, and he image segmenaion is he premise and guaranee of he classificaion of he foreign fiber. Image segmenaion is one of he primary sages in image processing and machine vision sysem, and i is also he precondiion of image analysis. The objecive of image segmenaion is o pariion he image ino meaningful conneced-componens o exrac he feaures of objecs [2]. According o he means of image segmenaion, he image segmenaion mehod can be divided ino hreshold mehod, boundary deecion mehod, area mehod and so on. Among hem, he hreshold mehod is he earlies sudied and used. A presen, due o he characerisics of clear physical meaning, obviously effec, easily implemening and good real-ime, he hreshold mehod became one of he mos commonly used image segmenaion mehod in image analysis, image recogniion and machine vision sysems. How o cie his paper: Chen, L.P., Chen, X.Y., Wang, S.L., Yang, W.Z. and Lu, S.K. (215) Foreign Fiber Image Segmenaion Based on Maximum Enropy and Geneic Algorihm. Journal of Compuer and Communicaions, 3, 1-7. hp://dx.doi.org/1.4236/jcc

2 The curren hreshold segmenaion mehod, according o he hreshold selecion crierion funcion ypes, can be divided ino maximum enropy mehod [3], maximum iner-class variance (Osu) mehod, cross enropy mehod [4], minimum error mehod, fuzzy enropy mehod and so on [5]. How o deermine he opimal hreshold is he key of he hreshold segmenaion mehod. Since he maximum enropy mehod does no require a priori knowledge, i is widely used in image segmenaion. Bu he maximum enropy mehod is hrough exhausive search of global opimal soluion, i is very complex and slow. In recen years, due o he fas random search capabiliy, geneic algorihm (GA) has been successfully applied in many fields, such as image processing, machine learning, parameer opimizaion and so on [6] [7]. In his paper, hrough combine GA wih maximum enropy mehod, we realized he fas segmenaion of he foreign fibers. The remainder of his paper is organized as follows. Secion 2 presens he maximum enropy mehod and he GA briefly. Secion 3 provides he proposed algorihm in deail. Resuls and discussions are followed in Secion 4. Secion 5 declares he conclusion. 2. Mehodology 2.1. Maximum Enropy Maximum enropy algorihm is a segmenaion mehod which based on he hisogram of he image [8]. Le x be he gray level of he image (from o L), p(x) is he probabiliy of he pixel which gray level is x in he image, hen he enropy of he image is represened as: L H= px ( )lg px ( ) (1) x= 1 Le he segmenaion hreshold is, which will divide he image ino wo pars: arge A and background B. Assuming ha he gray values which less han he hreshold of he pixels belong o he arge A, he gray values which greaer han or equal o he hreshold of he pixels belong o he background B, hen he probabiliy of pixels in he image are relaed o arge A and background B respecively is: Targe A: Background B: p p b a 1 = p (2) 255 i i = p (3) The enropy of he arge region and he background region are respecively defined as: 1 H ( ) = ( p p )lg( p p ) (4) a i a i a 255 H ( ) = ( p p )lg( p p ) (5) b i b i b The enropy of he whole image is defined as: H () = H() + H() (6) a The greaer he value of funcion H(), he more informaion of he arge and background represen, he more accurae he arge and he background area. The global opimal hreshold (le i be * ) is he value which make H() ake he maximum value, ha is, * can be expressed as: b = arg max H ( ) (7) 2.2. Geneic Algorihm Geneic algorihm (GA) is a parallel random search opimizaion mehod which simulaes naural geneic mechanism and biological evoluion heory. I is sochasic raher han exhausive. I can obain he global opimal 2

3 soluion wih large probabiliy. In addiion, GA does no require he evaluaion funcion o be monoone, so GA is very suiable for real-ime processing, which is no only easy o implemen, bu also has srong robusness. The basic idea of he algorihm is firs encoding he problem o be solved ino a sring (named chromosome). Each chromosome is a soluion o he being solved problem. Nex, GA calculaes he finess value of each individual in a group of chromosomes, hen chromosomes wih higher finess are seleced for crossover and muaion operaion and hen produce a new generaion wih higher finess. A las, he relaive opimal soluion is obained by coninuous ieraion. 3. Foreign Fiber Image Segmenaion Based on Maximum Enropy and Geneic Algorihm Through analysis he coon foreign fiber images, i is found ha he background of he coon layer has he absolue advanage in he whole image, while he arge is very small (Figures 2-5). Furhermore, he brighness and conras of he image are all poor. Using he radiional image segmenaion mehod (e.g. Osu), he segmenaion resuls are very poor. Therefore, we adop he maximum enropy hreshold mehod in his paper. However, he essence of he maximum enropy mehod is o obain he maximum value of he objecive funcion (Equaion (6)) in he gray space, he compuaion of he algorihm is very large, and i akes much more ime. In order o reduce he complexiy of he algorihm, we combine GA wih he maximum enropy in he foreign fiber image segmenaion. The maximum enropy funcion was used as he finess funcion of GA. By coninuous opimizaion, he opimal segmenaion hreshold is deermined. The flow char of he algorihm was shown as Figure Encoding and Design Finess Funcion Encoding a soluion of a problem ino a serial of genes, called chromosome, is very imporan when using GA. Various encoding mehods have been proposed for paricular problems o provide effecive implemenaion of GA, such as binary encoding, real number encoding, ineger or lieral permuaion encoding, or general daa srucure encoding [9]. In our research, we chose he binary encoding mehod. Since he gray level of he foreign fiber image has 256 gray level, so encoding he segmenaion hreshold wih 8 binary sring, and ake he enropy funcion of Equaion (6) as he finess funcion. Figure 1. Flowchar of he proposed mehod. 3

4 3.2. Populaion Iniializaion The iniial populaion for evoluion is creaed randomly. Individual in he iniial populaion is equivalen o he candidae soluion in he soluion space. If he populaion size is oo large, he compuaional complexiy is high; if he scale is oo small, he search space is limied, he search may sop a he immaure sage. Therefore, he populaion number is se o 2, he maximum number of ieraions is se o Selecion Operaion The selecion operaion, also known as he copy operaion, is o deermine which individual is geneic and which individual is eliminaed. The common mehods include roulee wheel mehod, he elie preservaion sraegy and he order selecion mehod. As he crossover and muaion operaions are performed, he opimal soluion can be easily los in an inermediae sep. So we adoped he mehod of 1% elie sraegy and 9% roulee wheel. Individuals wih he larges finess direcly ino he nex generaion, which can guaranee ha he algorihm converges o he global opimal soluion; he remaining 9% of he individuals are seleced according o he finess proporionae. The larger he finess of he individual, he greaer he probabiliy of being seleced. The probabiliy of he individual i is seleced o he nex generaion group is: n P = F F (8) i i i 1 where n is he populaion size, F i is he finess of he individual i Crossover Operaion Commonly used crossover operaions include single-poin crossover, double-poin crossover, muli-poin crossover, ec. In our research, we chose he single-poin crossover operaor, he crossover probabiliy is.8 and.6 respecively Muaion Operaion According o he muaion probabiliy, he individual's value is replaced by some oher gene values, which can increase he diversiy of he populaion, and can also improve he local search abiliy of GA. Various muaion operaions have been inroduced by researchers, such as inversion muaion, neighbor exchange muaion, ec. We chose inversion muaion in our research Probabiliy of Crossover and Muaion The probabiliies of crossover and muaion, denoed by P c and P m, affec seriously he search abiliy and convergence speed of GA [1]. For he sandard GA, P c and P m are all fixed. Bu in he early sage of evoluion, he individual difference is bigger, so we should increase P c and reduce P m in order o accelerae he evoluion process and a he same ime reducing he amoun of calculaion. In he laer sage of evoluion, he individual difference is small, so P m should be reduced and P c should be increased o avoid local opimal soluion. For he proposed algorihm, P c is.8 during he firs 5 generaions of he evoluion, so as o search he opimal soluion as soon as possible. In he second par of he evoluion, P c is.6. The muaion probabiliy P m is calculaed by: Pm log 2( g+ 1) f f Pm = Pm f f where P m is he iniial muaion probabiliy; f is he finess of he paren chromosome; f is he average finess of he chromosomes in he curren populaion [11] Terminae Crierion We se wo erminae crierions in our GA: 1) maximum generaion number. When he maximum number of ieraions is reached, he algorihm erminaes; 2) se he minimum number of ieraions is 1, when he ieraion (9) 4

5 number is larger han he value, check he difference beween he average finess of he curren populaion and he average finess of he las generaion wheher less han a minimum value ε (here ε =.1), once he condiion is saisfied, he ieraion erminaes. 4. Resuls and Discussions In order o verify he effeciveness of he paper s algorihm, we colleced 6 foreign fiber images which include 5 foreign fibers such as hair, feaher, hemp rope, polypropylene, cloh and so on. The image is size. The experimens are performed on over Malab7.1 plaform. We compared he resuls of he paper s mehod wih he resuls of he Osu s mehod. Figures 2-5 show a par of he experimenal resuls. I can be seen from Figures 2-5, for he original image, he coon layer background occupies he absolue advanage in he whole image, while he foreign fiber only accouns for a very small par, and he conras of he image is very low. Therefore, in many cases, he arge canno be separaed from he background by using he Osu mehod (e.g. Figures 2-4). For Figure 2 and Figure 3, alhough he arge was segmened, bu par of he background was divided ino he arge oo. The foreign fiber of Figure 4 was no exraced a all. So he reliabiliy of he Osu is very poor. In conras, his paper s mehod works well in mos cases. The exracion of he arge is accurae and complee. Table 1 liss he saisical segmenaion resuls of 6 foreign fiber images wih wo differen mehods. Figure 2. Color image of polypropylene fiber; The resul of he paper s mehod; The resul of Osu. Figure 3. Color image of cloh piece; The resul of he paper s mehod; The resul of Osu. 5

6 Figure 4. Color image of hemp rope; The resul of he paper s mehod; The resul of Osu. Figure 5. Color image of feaher; The resul of he paper s mehod; The resul of Osu. Table 1. Caparison of he segmenaion resuls by wo differen mehods. Image name Image number The proposed mehod Segmenaion accuracy Hair 1 9% 2% Feaher 15 1% 1% Polypropylene 1 1% 5% Cloh 15 1% 6% Hemp rope 1 1% 2% Osu The resuls shown in Table 1 indicaed ha for differen foreign fiber images, he segmenaion accuracy of he proposed mehod is very high, i has srong adapabiliy. While for he Osu mehod, he segmenaion accuracy is higher when he area of he arge is larger (such as he feaher images, he segmenaion accuracy is 1%). When he foreign fiber s area is small, he segmenaion accuracy is very low (such as hair and wine images, he accuracy rae is only 2%). So, for low conras images, he mehod of combining he maximum enropy and geneic algorihm can effecively improve he image segmenaion accuracy. 6

7 5. Conclusions Due o he low brighness, low conras and small proporion of he arge of he foreign fiber images, we adoped he image segmenaion based on maximum enropy and geneic algorihm. Experimenal resuls show ha he adoped algorihm is more accurae han he radiional Osu mehod, and he speed is fas and adapable, which mees he real-ime requiremens of he foreign fiber deecion sysem. Nex work: for he deecion of whie foreign fibers (such as plasic sheeing, plasic film, ec.), he accuracy of he segmenaion algorihm is poor, here is sill o be furher improved. Acknowledgemens The auhors hank Hebei Naural Science Foundaion (F ), he Minisry of Science and Technology of he People s Republic of China (213DFA1132), for heir financial suppor. References [1] Yang, M.H. (214) Hazards and Prevenion of Foreign Fiber in Coon. Jiangsu Texile, 4, (In Chinese wih English Absrac) [2] Zhang, Y.J. (213) Image Engineering (II) Image Analysis. Tsinghua Universiy Press, Beijing. [3] He, Z.J. and Wang, H.F. (21) Chinese Word Sense Disambiguaion Based on Maximum Enropy Model wih Feaure Selecion. Journal of Sofware, 21, hp://dx.doi.org/1.3724/sp.j [4] Wu, Y.Q. and Zhang, X.J. (211) Two-Dimensional Symmeric Cross-Enropy Image Thresholding. Journal of Image and Graphics, 16, [5] Wu, Y.Q., Meng, T.L. and Wu, S.H. (215) Research Progress of Image Threshold Mehods in Recen 2 Years ( ). Journal of Daa Acquisiion and Processing, 3, [6] Ou, P. and He, D. (211) 2-D Maximum Enropy Mehod of Image Segmenaion Based on Geneic Algorihm. Journal of Compuer Simulaion, 1, [7] Guo, M.S. and Liu, B.H. (28) 2-D Maximum Enropy Mehod in Image Segmenaion Based on Chaos Geneic Algorihm. Compuer Technology and Developmen, 18, [8] Cao, J.N. (212) Review on Image Segmenaion Based on Enropy. Paern Recogniion and Arificial Inelligence, 25, [9] Zhang, C.Q., Zheng, J.G. and Qian, J. (211) Comparison of Coding Schemes for Geneic Algorihms. Applicaion Research of Compuers, 28, [1] Cao, D.Y. and Cheng, J.X. (21) A Geneic Algorihm Based on Modified Selecion Operaor and Crossover Operaor. Compuer Technology and Developmen, 2, [11] Yang, W.Z., Li, D.L. and Zhu, L. (21) An Improved Geneic Algorihm for Opimal Feaure Subse Selecion from Muli-Characer Feaure Se. Journal of Agriculural Machinery, 38,

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