Improved Graph-Based Image Segmentation Based on Mean Shift 1

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1 03 8th Intenational Confeence on Communication and Netwoking in China (CHINACOM) Impoved Gaph-Baed Image Segmentation Baed on Mean Shift Jianwen Mo, Chaoxuan Wang,Tong Zhang, Hua uan School of Infomation and Communication Engineeing Guilin Univeity of Electonic Technology Guilin,China Abtact Accoding to the poblem of ove-egmentation uing gaph-baed image egmentation(gbis), combined with Mean Shift, the pape popoed an impoved gaph-baed image egmentation algoithm(igbis) uing Lab colo pace. Fitly, the ouce image wa moothed by mean hift; Second, the mean hift image wa conveted fom RGB colo pace to Lab colo pace; Then, the weight function in GBIS method wa impoved by a contant S that we intoduced to contol the colo diffeence degee; Finally, two kind of Algoithm evaluation method (GCE and VOI) wee ued to evaluate the impoved method. The expeiment eult how that the impoved method depeed ove-egmentation, eceived bette egmentation eult. Keywod-Segmentation;Mean Shift;Lab colo pace I. INTRODUCTION Image egmentation which i the bae development in the field of image poceing i the foundation of the highe laye image poceing and ha impotant ole in image etieval and moving taget detection. Howeve, with the extenive application of gaph theoy, image egmentation baed on gaph theoy in ecent yea ha become one of eeach hot pot. Fo example, MST[], Nomalized Cut[], Io[3], GapCut[4],Dynamic Region Meging [5], andom walke[6],etc. GBIS[7](Efficient Gaph-baed Image Segmentation) method i popoed by Felzenzwalb and Huttenloch in 004 yea. The GBIS algoithm un in time nealy linea in the numbe of gaph edge and i alo fat in pactice. An impotant chaacteitic of the method i it ability to peeve detail in low-vaiability image egion while ignoing in highvaiability egion. At the ame time, the egmentation eult ae neithe too coae no too fine egmentation eult. Howeve, by expeiment and theoy tudy we find that GBIS algoithm ignoe the impact of uing RGB colo pace on the pefomance of egmentation. What moe, the GBIS method ue imple weight function, which lead to oveegmentation quetion in egmentation eult. Accoding to the poblem in GBIS method, combined with Mean Shift, the pape popoed an impoved GBIS image egmentation algoithm (IGBIS) uing Lab colo pace. II. IMPROVED GRAPH-BASED IMAGE SEGMENTATION BASED ON MEAN SHIFT (IGBIS) The expeiment deign of impoved GBIS method baed on mean hift i illutated in Figue, Input Image evaluation index Gauian Smooth Segmentation Reult Mean Shift Region Meging Lab tanfom Convet Image into gaph Fig. Impoved Gaph-Baed Image Segmentation Expeiment Deign (IGBIS) The RGB colo image i ead and egaded a the ouce image. Fitly, the image i moothed by Gauian to educe the effect of noie on image egmentation. Secondly, the mean hift i applied and the mean hift image i eceived. Thidly, the Mean Shift image i conveted fom RGB pace to Lab colo pace. At the ame time, combined with the Lab colo pace, the image i conveted to gaph, and then the egmentation eult i achieved by egion meging. Finally, the egmentation eult i evaluated though evaluation index of image egmentation. A. Mean Shift Image Smooth Mean Shift algoithm i a kind of image poceing method baed on iteative poce, the algoithm pinciple ha uch tep, fit of all, the pixel to calculate it aveage deviation; Then, move the cuent point to the aveage deviation, and viewed the point a the tating point of the new migation, continue to move until the end of eache a cetain condition, o the algoithm i alo called the mean hift [8]. Accoding to the pape [8]' defined, C x x Kh ( x) = k k p hh h h () Whee x i the patial pat, x i the ange pat of a featue vecto, K ( x) the common pofile ued in both two domain, h and h the employed kenel bandwidth, and C the coeponding nomalization contant. In pactice, an Epanechnikov o a (tuncated) nomal kenel alway povide atifactoy pefomance, o the ue only ha to et the bandwidth paamete h = ( h ), which, by contolling the ize of the kenel, detemine the eolution of the mode detection. Fund to Suppot: Natual Science Foundation of Guang Xi(03GXNSFAA0933,0GXNSFAA0533, 0GXNSFBA05304, 03GXNSFDA09030); Scientific Reeach Poject of Guang Xi Depatment of Education (00ZD040,004LX46) IEEE

2 A pape [8] defined, let xi and z i, i =,,..., n, be the d- dimenional input and filteed image pixel in the joint patialange domain. Fo each pixel,. Initialize j= and yi, = xi.. Compute y i, j +, until convegence, y = y ic,. Fig. Souce Image 3. Aign zi = ( xi, yi, c). B. Chaacteitic and Tanfom of Lab Colo Space In thi pape, we ue the L a b colo pace intead of the RGB colo pace in oiginal algoithm (GBIS algoithm), ha the thee eaon: fit, the L a b colo pace ha wide colo gamut. All type of colo which can be peceived by human eye can be fomed in L a b pace. Second, unlike the RGB and CMK colo model, L a b colo i deigned to appoximate human viion, and it apie to peceptual unifomity. Thid, the L a b colo model alo make up fo the deficiency of the RGB colo model of colo ditibution. The aw image data ae given in the RGB pace, the method in [9], the definition of Lab i baed on an intemediate ytem, known a the CIE XZ pace, which i deived fom RGB a follow, X = R G B = 0.67R G B Z = R G B Baed on thi definition, Lab i defined a follow, Whee L f () t = = 6 f ( ) 6 n a = 500[ f ( X ) f ( )] X n n b = 00[ f ( ) f ( Z )] n Z n /3 t if t > t + 6 / 6 othewie X n, n and Z n epeent a efeence white, X Z (5) t,, X n n Z n Uing the above tanfomation way, Fig. i tanfom to Lab image a Fig.3, () (3) (4) Fig.3 Lab Image C. Gaph-Baed Image Segmentation (GBIS) Image egmentation baed on gaph technique geneally epeent the poblem in tem of a gaph G= {V, E}, whee each node v i V coepond to pixel in the image, and the edge in E connect cetain pai of neighboing pixel. The pat of gaph-baed image egmentation, we ued the method in [7] (GBIS). Fitly, impoved weight function i popoed combining with L a b colo pace. Secondly, egion meging the method in [7] i adopted to get egmentation eult. In L a b colo pace, combining the algoithm in [7] i eay to appea ove-egmentation phenomenon, we edefine the weight function in [7]. We add a paamete ( fo contant, default: = ) to contol the diffeence between two diffeent pixel, the geate the value of that make the diffeence degee between the two diffeent pixel i malle, the geate the chance of aigned to the ame aea. The impoved weight function a follow, ( L L) + ( a a) + ( b b) we ( ) = w(( v, v)) = (6) V. Hee, L, a and b mean thee colo component of vetex The method in [7], Fitly, weight function i defined, howeve, in thi pape, impoved weight function i ued, a function (6); Secondly, the Max weight in two diffeent component i gained by function(7) and (8). Then, the minimum intenal diffeence i defined a function (9). Finally, the egion meging pedicate i defined a function (). Int( C ) = max w( e) e MST ( C, E) (7) diff ( C, C ) = min w(( v )) (8) v C C,( v ) E If thee i no edge connecting C, C,GBIS method let diff ( C, C ) =. MIntC (, C ) = min( IntC ( ) + TC ( ), IntC ( ) + TC ( )) (9) 686

3 The function T i defined a, K T ( C ) = (0) C Whee C denote the ize of C, and K i ome contant paamete. III. tue if diff ( C, C) MInt ( C, C) DC (, C) = fale ot he wie THE EXPERIMENT AND EVALUATION PREPARE () Combining OpenCV.0, unde the L a b pace, we compae impoved IGBIS method and GBIS method unde the ame paamete in VS005 platfom. In the expeiment, we adopt diffeent Mean Shift paamete value, with the aid of GCE (Global Conitency Eo) [0] and VOI (Vaiation of Infomation) [] image egmentation evaluation index, fom two apect of ubjective and objective compaion and evaluation. All the image in the expeiment ae povided by Univeity of Califonia Compute Viion Goup []. (c) ( h )=(8,4) (d) ( h )=(8,8) Figue. Fig.5 Unde diffeent h and h egmentation image Fom Fig.4 and Fig.5, we can be concluded that h and h in the Mean Shift algoithm not only diectly affect the moothing image but alo have a cucial impact on egmentation image. B. Segmentation Reult A. The influence of diffeent paamete in Mean Shift In Mean Shift algoithm and h have a majo impact to egmentation eult. Unde diffeent h and h mooth image a Fig.4. Unde diffeent h and h egmentation image a Fig.5. (a) Souce Image )=(8,4) (d ) Ideal Reult Figue 3. Fig.6 Expeiment (a) ouce image (b) ( h )=(4,4) (a) Souce Image (c) ( h )=(8,4) (d) ( h )=(8,8) Figue. Fig.4 Unde diffeent h and h mooth image )=(4,4) (d ) Ideal Reult Figue 4. Fig.7 Expeiment (a) GBIS (b) ( h )=(4,4) (a) Souce Image 687

4 )=(4,) (d ) Ideal Reult Figue 5. Fig.8 Expeiment 3 GCE Value GBIS IGBIS Expeiment Num (GCE the malle the bette) Fig. GCE Index (a) Souce Image VOI Value GBIS IGBIS Expeiment Num (VOI the malle the bette) )=(,) (d ) Ideal Reult Figue 6. Fig.9 Expeiment 4 (a) Souce Image )=(,4) (d) Ideal Reult Figue 7. Fig.0 Expeiment 5 In five expeiment, (a) i ouce image; (b) i GBIS egmentation eult; (c) i Impoved eult; (d) i ideal eult fom Fig.6 to Fig.0. All the ideal eult in evey expeiment ae povided by Univeity of Califonia Compute Viion Goup []. C. Image Segmentation Evaluation Index Fig. and Fig. how the GCE and VOI in five expeiment. Fig. VOI Index Fom Fig. and Fig. we can know that the egmentation eult which ae achieved by impoved GBIS method (IGBIS) ae cloe to ideal eult than by GBIS method. Fom all the expeiment we can know that impoved GBIS method combined with Mean Shift effectively inhibit oveegmentation in ouce GBIS method and eceive bette egmentation eult, that have thee eaon: () The Mean Shift mooth effectively educe the colo diffeence between pixel and moe conducive to pixel of meging, which effectively olve the ove-egmentation poblem and impove the image egmentation eult; () Uing the L a b colo pace, impoved GBIS method effective avoid RGB colo pace impact to egmentation eult.(3) Impoved weight function in impoved GBIS add a paamete to contol the diffeence between two diffeent pixel, which benefit to pixel and egion meging. IV. CONCLUSION Accoding to the poblem of the GBIS method which the eult ove-egmentation, combined with Mean Shift, the pape popoed an impoved GBIS image egmentation algoithm(igbis) uing Lab colo pace. At the ame time, the pefomance of impoved GBIS method i veified by lage numbe of expeiment, and the paamete of Mean Shift ae dicued in the expeiment. Finally, GCE and VOI index ae ued to evaluate and analyi the impoved GBIS method. Expeiment how that unde the ame condition, impoved GBIS algoithm effectively uppee the ove-egmentation phenomenon exiting in the oiginal algoithm, achieved bette eult. REFERENCES [] Zhiguang Cao; Xuexi Zhang; Xuezhu Mei. Unupevied Segmentation fo Colo Image Baed on Gaph Theoy[J]. IEEE Intelligent Infomation Technology Application, 008,:

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