Image Saliency Detection Algorithm Based on Super Pixels Partition Wenwen Pana, Xiaofei Sunb, Xia Wangc, Tao Xud, Lina Gonge

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1 4th Internatonal Conference on Advanced Materals and Informaton Technology Processng (AMITP 2016) Image Salency Detecton Algorthm Based on Super Pxels Partton Wenwen Pana, Xaofe Sunb, Xa Wangc, Tao Xud, Lna Gonge College of Informaton Scence and Engneerng Zaozhuang Unversty,Zaozhuang,277160,Chna a panwenwen@vp.qq.com, b @qq.com, c @qq.com, d @qq.com, e @qq.com Keywords: Salency detecton, super pxel, salency map, salent obect. Abstract. Ths paper proposes an mage salency detecton algorthm based on super pxels partton. The algorthm frst uses the smple lnear teratve clusterng method to dvde the mage nto multple super pxels, and then accordng to the rules of global salency to calculate the salency level of each super pxel, fnally the salency map of the whole mage s obtaned. Expermental results show that the proposed approach can extract salency map from the mage of dfferent sze wth dfferent types of salent obect, and the effect s better. Introducton The most mportant part of the mage are often concentrated n the small key regons, these regons called salent regon, mage salency detecton task s to fnd out whch regons of the mage can attract the attenton of human vsual more[1]. Salency map s used to reflect the dfferent emphass of human eye to dfferent part of the mage, the brghter regons can cause more attenton of the human eye. Wth the loss of the brghtness, the emphass degree caused n turn reduce. Image salency detecton can be used for content transmsson, target detecton, mage restoraton, etc. The exstng salency detecton methods are grouped nto space-doman-based and frequency-doman- based [2].Algorthms based on space doman are [3][4][5][6], the algorthms based on frequency doman are [7][8]. A salency detecton algorthm based on super pxels partton s proposed n ths paper. Super pxel algorthm adopts the method of clusterng groups pxels nto meanngful atomc regons nstead of pxel grd wth fxed sze and shape, n order to reduce computatonal complexty. And then accordng to the rules of global salency to determne salency degree of each super pxel. Fnally generate salency map wth the salency degree of each super pxel. Pxels Partton of Regonalzaton Exstng super pxels partton algorthm can be grouped nto two broad categores: graph-based algorthms and gradent-ascent-based algorthms. Super pxel partton algorthms based on graph theory have [9,10,11] and based on the gradent descent algorthm have[12,13,14,9]. Ths artcle uses the smple lnear teraton method of super pxel partton s descrbed n the lterature [15], namely the SLIC method. SLIC s a super pxel partton algorthm based on k - means clusterng algorthm. It has two mportant features: one s that through lmtng the search space to an area whch sze s proportonal to the the sze of the super pxel to reduce the number of the dstance calculaton durng the optmzaton process.the other s to weght the color and spatal proxmty to control super pxel s sze and close degree. Specfc algorthm s as follows: 1) Intalze clusterng center ncludng Lab space, and x, y pxel coordnates, and unform the dstrbuton of the clusterng center accordng to the number of pxels fxed. 2) In order to prevent the clusterng center from fallng on the border the clusterng center wll be moved to the smallest gradent place wthn the 3 * 3 neghborhood The authors - Publshed by Atlants Press 433

2 3) Wthn 2s * 2s neghborhood of the cluster center dstrbue the matchng pont for each cluster center accordng to the set dstance between each pont and the clusterng center to calculate. 4) Calculate the L1 dstance between new clusterng center and the clusterng center before. Accordng to the threshold value to udge whether need to dsturb the clusterng center, whch s always teratve computaton untl the dstance between the clusterng center s less than a certan threshold. 5) End of operaton Global Salency Rules Determnaton The super pxels after mage partton obtan regonal characterstcs. The super pxels nstead of pxels to be calculated the degree of each part can greatly reduce the computng complexty. In order to determne the salency degree of each super pxel compared wth other super pxels n the whole mage, we make two global salency rules accordng to the characterstcs of human vsual attenton as follows, 1) The regon wth larger contrast of color or brghtness n the mage can cause human's attenton. 2) The nfluence of any other super pxels to one super pxel n the whole mage ncreases wth the color dfference between them ncreasng, but decreases wth the ncrease of dstance dfference. Accordng to global salency rules, the degree of any super pxel nfluence on super pxel s expressed wth formula (1). N dcolor, df, (1) cd, poston N s the number of super pxels after partton, c s the coeffcent to adust the centrod dstance. The RGB color space s evenly dstrbuted and Lab color space s evenly dstrbuted, so the latter s more sutable for calculatng the color dstance of the mage. In order to further reduce the computaton complexty, quantfy each mage wth 12 dfferent grades n each color channel to thn the color space, and then calculate color dstance between the two super pxels, as shown n formula (2). color, N1 N2 p cn, p c 1 n, d c 2 n,, c (2) 1 n, 2 Among them, pcn, 1 and n, 2 d n11n21 p c s respectvely the probablty of n 1 and n 2 color wthn super pxels, after the color sparse. N 1, N 2 s respectvely the total number of color wthn super pxel, after the color sparse. Use barycentrc coordnates Eucldean dstance of two super pxels to ndcate the dfference of locaton between two super pxels, sad as shown n formula (3)., 2 2 d x x y y (3) poston Among them, x, y and x, y s respectvely the x, y centrod coordnate of super pxel,. The fnal salency value of super pxel s equal to the sum of the value that other super pxels 1, 2,, N, 且 nfluence on t, represented by formula (4). N 1, salencyvalue df, (4) 434

3 Salency Map Extracton and Comparatve Analyss The method proposed n ths paper s sutable for mages wth dfferent sze, and t can obtan good effect to extract salency maps from mages contanng dfferent types of salent obects. Not only can completely extract salent obect, but also can well dstngush the salency degree of each part of the salent obect. In ths paper, the salent obects are classfed nto four sorts: salent obect wth the color close to background s, small salent obect, bg salent obect and multple salent obects. Experment on mage lbrary ImgSal [16] and select one mage of all knds on ther behalf. The orgnal mage and the correspondng salency map s shown n Fgure 1. (a) salent obect wth the color close to background s (b) small salent obect (c) bg salent obect (d) multple salent obects Fgure 1 Salency map extracted on the bass of the SLIC super pxels partton Compare the method proposed n ths paper wth the exstng several salency map extracton method, the result s shown n Fgure 2. The salency map extracted by IT algorthm [17] s some dscrete ponts. It cannot fully reflect the characterstcs of the shape of the salent obect. There s a wdespread problem that hollow exsts n the salent obect wthn the salency map extracted by MZ algorthm, GB algorthm and SR algorthm because only outlne nformaton of salent obect s extracted. AC algorthmand IG algorthm can solve the problem of hollow effectvely, but ndvdual mage background has hgher salency degree. The method proposed n ths paper can better solve the problem of hollow, effectvely suppress the nfluence of the background and well dstngush the salency degree of each part of the salent obect. (a)orgnal mage (b)it (c)mz (d)gb (e)sr (f)ac (g)ig (h)ths paper Fgure 2 The comparson of salency map extracted by dfferent method 435

4 Concluson and Prospect The salency map extracton algorthm proposed n ths paper uses the SLIC method to dvde the mage nto dfferent super pxels frstly, and then accordng to the rules of global salency to determne salency degree of each super pxel. It can extract salency map from the mage of dfferent sze wth dfferent types of salent obect and acheve better effect than exstng algorthm. Next, the method proposed n ths paper wll be consdered to apply to mage retreval, mage restoraton, etc. Acknowledgments Ths work was fnancally supported by Zaozhuang unversty scentfc research fund youth proect. (Grant No.2015QN12) References [1] Wenwen Pan, Xaofe Sun, Xa Wang, We Zhang. Bref Analyss on Typcal Image Salency Detecton Methods [C]. Internatonal Conference on Informaton Scences, Machnery, Materals and Energy 2015: [2] Wenwen Pan, Xaofe Sun, Xa Wang, We Zhang. Bref Analyss on Typcal Image Salency Detecton Methods. Internatonal Conference on Informaton Scences, Machnery, Materals and Energy (ICISMME), [3] L. Itt, C. Koch, and E. Nebur. A model of salency-based vsual attenton for rapd scene analyss. PAMI,20(11): ,1998. [4] Y.-F. Ma and H.-J. Zhang. Contrast-based mage attenton analyss by usng fuzzy growng. ACM MM [5] J. Harel, C. Koch, and P. Perona. Graph-based vsual salency. NIPS,19: , [6] R. Achanta, F. Estrada, P. Wls, and S. Süsstrunk. Salent regon detecton and segmentaton. ICVS [7] R. Achanta, S. Hemam, F. Estrada and S. Süsstrunk, Frequency-tuned Salent Regon Detecton, IEEE Internatonal Conference on Computer Vson and Pattern Recognton (CVPR 2009), pp , [8] X. Hou and L. Zhang. Salency detecton: A spectral resdual approach. CVPR [ 9 ] Yuhang Zhang, Rchard Hartley, John Mashford and Stewart Burn, Superpxels va Pseudo-Boolean Optmzaton, Internatonal Conference on Computer Vson (ICCV), [10] Mng-Yu Lu, Tuzel, O., Ramalngam, S., Chellappa, R., Entropy Rate Superpxel Segmentaton, CVPR, [ 11 ] Pedro Felzenszwalb and Danel Huttenlocher. Effcent graph-basedmage segmentaton. Internatonal Journal of Computer Vson (IJCV),59(2): , [12] A. Levnshten, A. Stere, K. Kutulakos, D. Fleet, S. Dcknson, and K. Sddq. Turbopxels: Fast superpxels usng geometrc flows. IEEETransactons on Pattern Analyss and Machne Intellgence (PAMI),2009. [13] A. Vedald and S. Soatto. Quck shft and kernel methods for mode seekng. In European Conference on Computer Vson (ECCV), [14] D. Comancu and P. Meer. Mean shft: a robust approach toward featurespace analyss. IEEE Transactons on Pattern Analyss and MachneIntellgence, 24(5): , [15] Radhakrshna Achanta, Appu Sha, Kevn Smth Aurelen Lucch, Pascal Fua, and Sabne Susstrunk[J]. SLIC Superpxels Compared to State-of-the-art Superpxel Methods. Joural of Latex Class Fles, Vol. 6, NO. 1, [16] AN X J, LRTINE L, HE H G, et al. ImgSal: A benchmark for salency detecton V1.0 [EB/OL]. (2001) [ ]

5 [17] L. Itt, C. Koch, and E. Nebur. A model of salency-based vsual attenton for rapd scene analyss. PAMI,20(11): ,