An Improved Computational Approach for Salient Region Detection

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1 JOURAL OF COPUTERS, VOL. 5, O. 7, JULY An Improved Computatonal Approach for Salent Regon Detecton Qaorong Zhang, 2, Habo Lu, Jng Shen, Guochang Gu College of Computer Scence and Technology, Harbn Engneerng Unversty, Harbn, Chna Emal: zhangqaorongs@sna.com Humn Xao 2 2 Henan Unversty of Fnance and Economcs, Zhengzho Chna Emal: humnxao@26.com Abstract Salent regon detecton n mages s very useful for mage processng applcatons lke mage compressng, mage segmentaton, object detecton and recognton. In ths paper, an mproved approach to detect salent regon s presented. The proposed method can generate a robust salency map and extract salent regons wth precse boundares. In the proposed method, local salency, global salency and rarty salency of three knds of low-level feature contrast of ntensty, color and orentaton are used to compute the vsual salency. A new feature ntegraton strategy s proposed n ths paper. Ths method can select features and compute the weghts of the features dynamcally by analyzng the effect of dfferent features on the salency. Then a more robust salency map s obtaned. It has been tested on many mages to evaluate the valdty and effectveness of the proposed method. We also compare our method wth other salent regon detecton methods and our method outperforms other methods n detecton results. Index Terms salent regon, salency map, vsual attenton, mage processng, feature ntegraton I. ITRODUCTIO Wth the development of nformaton technology, mages become the man resource of nformaton. How to analyze and process the huge mage resources effcently and effectvely become the urgent problem. Durng the research, people fnd that most of the nformaton often les n some small key regons n the mage. These key regons are so-called salent regons or regons of nterest and so on. If we can extract these salent regons correctly and pay more attenton to them, we can reduce the computatonal complexty and mprove the speed of mage processng sgnfcantly. There have been some salent regon detecton methods. ost of them can successfully extract the salent regons n some crcumstances but fal n some mages. The most mportant step to detect salent regons s to compute the salency of every part n the mage. ost of the exstng methods to compute salency are based on feature ntegraton theory that the salency of every part n the mage can be ndcated by the dfference Correspondng author: Qaorong Zhang. of some feature values of t and ts surroundngs. Itt et al. have developed a bologcally nspred computatonal model for computng vsual salency [] [2]. They compute salency maps usng center-surround operaton on features of ntensty, color and orentaton at dfferent scales. Then an ntegrated salency map s obtaned by combnng these feature salency maps. But ths method can only gve the salent locaton and t cannot gve the boundary of salent regons. They gave four combnaton strateges n [3]. The weghts of dfferent features were equal n the nave lnear combnaton method. So the results were not satsfyng. The method of lnear combnaton wth learned weghts was better but t requred a pror knowledge of the salent regons. The global non-lnear normalzaton method and the teratve non-lnear method both used a local competton strategy n one feature map. But those methods ddn t analyze the effects of dfferent features and just summed these normalzed feature maps together. a et al. proposed a computatonal method to detect salent regons by computng color contrast of every pxel n [4]. But they only use color feature. It s approprate when the color feature s the most useful feature for the mage. It can t gve the rght salency result f the actual contrast resdes n other features. The extracted salent regons usng fuzzy growng often ncluded the backgrounds or some wrong locatons. Zhang et al. used the smlar method to compute the feature contrast n [5]. They used multple features lke ntensty, color and texture. But they ddn t analyzng the dfferent effect of each feature and just summed them up. Hou et al. presented a spectra resdual method to compute vsual salency n [6]. Ths method s smpler and more effcent than other exstng method. But only ntensty feature was used n the method. It could not get the rght result f ntensty was not the useful feature. And t could detect the rght regons f the background was clustered and the salent regon was flat [7]. Therefore, most of the exstng methods have some shortcomngs n salency computng and feature ntegraton strategy. Frstly, the salency maps generated by those methods often provde some wrong salent locatons whch are not really salent. Otherwse, some regons are really salent but ther salency values are very 200 ACADEY PUBLISHER do:0.4304/jcp

2 02 JOURAL OF COPUTERS, VOL. 5, O. 7, JULY 200 low. Some methods have been presented to solve ths problem [8] [9]. But these methods need mage segmentaton or learnng and the complexty s hgh [0][]. Secondly, dfferent features have dfferent effects on vsual salency. So t s necessary to gve a strategy to decde what features are useful and ntegrate them wth dynamc weght. Some combnaton strateges can be found n [2] [3]. But the results were not satsfyng. Yqun Hu et al. proposed a more ntegrated feature combnaton strategy n [4]. However, they used the sze of a convex hull to approxmate the area of salent ponts and a man feature map was referenced to evaluate other feature maps. These were not reasonable n some crcumstances. In ths paper, an mproved method for detectng salent regons s proposed. The proposed approach manly ncludes two mprovements. Frstly, the method to compute vsual salency s mproved. A more robust salency map can be generated usng ths method. Secondly, a novel feature ntegraton strategy s presented. Dfferent feature maps are analyzed and ntegrated together usng dynamc weghts. Ths paper s organzed as follows. Secton II descrbes the outlne and detals of the proposed salent regons detecton method. Secton III gves some expermental results and dscusson. Secton IV presents our conclusons and prospects. II. IPROVED APPROACH FOR SALIET REGIO DETECTIO Fg. shows the framework of our proposed approach for salent regon detecton. Frstly, low-level vsual features lke ntensty, color and orentaton are extracted. Then vsual salency of each feature map s computed to get some feature salency maps. These feature salency maps are ntegrated to get an ntegrated salency map usng dynamc weghts accordng to ther dfferent effects on salency. Then, the ntegrated salency map s smply segmented usng a threshold and a bnary mage s generated. Fnally accordng to the bnary mage, the salent regons are extracted. A. Vsual Feature Extracton Early vson features have sgnfcant effects on salency of the regons n the mage. If the regon has ntensty nformaton that s dfferent from ts surroundng ntensty nformaton, the regon s remarkable n the ntensty feature. If the regon has dfferent color nformaton from ts surroundng color nformaton, the regon s salent n the color feature map. In addton, edge, shape and orentaton also have effects on the vsual salency. Snce the retna cells can extract ntensty, color and orentaton nformaton from natural scenes, we use these factors as the basc features of the salency map []. Because HSI (Hue, Saturaton and Intenst color space s consstent wth human color percepton system and s better than RGB color space, the nput mage s transformed from RGB space to HSI space usng (). Then we use I channel n () to represent ntensty feature of the nput mage. H (hue) channel and S (saturaton) channel are used to descrbe the color feature of the mage. Four orentaton feature maps can be obtaned by flterng the ntensty feature map usng four Gabor flters wth orentaton 0, 45, 90, 35 respectvely. F H [90 Arc tan( ) + {0, G > B;80, G < B}] mn( R, G, B). () S [ ] I ( R + G + B) I 3 2R G B F G B Input mage Vsual feature extracton Intensty Color Orentaton Intensty salency Computng vsual salency Color salency Feature ntegraton Salency map Salent regon(s) Orentaton salency Fgure. Block dagram of salent regons detecton B. Computng Vsual Salency After extractng low-level vsual features, vsual salency of every part n each feature map s requred to compute. Exstng methods to compute salency always generate some wrong salency results. For example, the computed salent areas often locate on the boundares between salent object and background where feature value changes sharply. Ths can be seen n the top row of Fg. 2. In addton, the salency value of a clustered background s hgh but the salency value of a flat foreground area s low n some crcumstances whch are shown n the bottom row of Fg. 2. The frst column shows the nput mages. The second column shows the salency maps generated by Itt s method based on centersurround operaton []. The salency maps computed by Hou s method based on spectra resdual are shown n the thrd column [6] and Achanta s results based on global feature contrast are shown n the fourth column [3]. 200 ACADEY PUBLISHER

3 JOURAL OF COPUTERS, VOL. 5, O. 7, JULY Therefore, to get a more robust salency map our proposed method consders three knds of salency whch are local salency, global salency and rarty salency. Some examples are shown n Fg. 3. The mages n the top row are orgnal mages and ther ntensty feature maps are shown n the mddle row. The local salency maps of the ntensty feature maps are shown n the bottom row. Fgure 2. Example of ncorrect salency map. ) Local salency: Whether a regon s salent n the mage or not depends on the dstnctness between tself and ts envronment [4]. Here we use local salency to represent the dfference between a regon and ts surroundngs. Dfferent from other methods whch compute salency n spatal doman, we analyze the local salency n frequency doman. In frequency doman, an mage can be decomposed nto magntude spectrum and phase spectrum. It has been dscussed n [5] [6] that phase spectrum s very mportant n mage reconstructon. If we reconstruct the mage wth phase spectrum only or wth a random changed magntude spectrum, the reconstructed mage can reserve the structure nformaton and less dstort the orgnal mage [5]. But f we reconstruct the mage wth a random changed phase spectrum, the reconstructed mage severely dstort the orgnal mage. It s ndcated that phase spectrum represents the nformaton of value changng at each poston whereas magntude spectrum represents the partcular value at each poston. Because we only care the change of feature value, we reconstruct the mage wth phase only to elmnate the nfluence of magntude spectrum and get the local salency. The process s descrbed as follows. a) Dscrete Fourer transform: Transform the nput mage from spatal doman nto frequency doman usng (2). Fgure 3. Example of local salency map 2) Global salency: Fg. 3 shows that local salency can ndcate the places where feature values change and gve them hgh salency value. But only consderng local salency s not enough because hgh local salency values often le n boundares between salent areas and the background. The salency values nsde the salent object are low. Ths can be seen from Fg. 3. So we use global salency as well. Global salency map of a feature map can be computed usng (5). S f Global avg e * favg favg x y. (5) Fg. 4 shows some global salency maps. The mages n the top row are orgnal mages and ther ntensty feature maps are shown n the mddle row. The global salency maps of the ntensty feature maps are shown n the bottom row. F( m x y R( + e j2πux ji( e j2πvy. (2) Where f(x, means the feature map wth dmenson *. The values F( are the DFT coeffcents of f(x,. b) Extract phase spectrum: Compute the phase spectrum usng (3). I( P ( arctan( ). (3) R( c) Compute local salency: Reconstruct the mage wth phase spectrum only usng (4) and get the local salency map. S local * u v I( e j 2πux e j 2πvy. (4) Fgure 4. Example of global salency map 3) Rarty salency: Rarty salency means the less a feature value occurs the more possble t belongs to a salent area. The areas that have novel and rare feature values often attract people s attenton and become salent areas n the mage. The easest method to measure the rarty of a feature value s to count the number t occurs n the mage. The hgher the number s, the lower the salency s. The hstogram of an mage s an adequate statstcal tool to count the number of each pxel feature value occurs. So the rarty salency can be computed usng (6). 200 ACADEY PUBLISHER

4 04 JOURAL OF COPUTERS, VOL. 5, O. 7, JULY 200 S Rarty. (6) hst( ) Where f(x, s the feature value of pxel (x, n the feature map and hst( ) s the hstogram of the feature map. Fg. 5 shows some rarty salency maps. The mages n the top row are orgnal mages and ther ntensty feature maps are shown n the mddle row. The rarty salency maps of the ntensty feature maps are shown n the bottom row. mportance of the feature conspcuty maps and compute ther weghts. Intensty map Local salency Global salency Rarty salency w0.088 w w Conspcuty map Fgure 6. Example of feature conspcuty map Fgure 5. Example of rarty salency map 4) Feature salency map: Fnally, local salency map, global salency map and rarty salency map need to be combned nto a feature conspcuty map. Dfferent weghts need to be appled to each salency result. To compute the weght of each salency result, the varance V of each salency result s calculated frst. The hgher the varance s, the more mportant the salency result s [7]. Then the feature conspcuty map can be generated usng (7). V w C F * V 3 w V * S + w y + w j j Local 2 * S Global * 3 * S Rarty.(7) The process of generatng ntensty conspcuty map s shown n Fg. 6. Frstly, the ntensty feature of the nput mage s extracted. Then local salency, global salency and rarty salency of the ntensty map are calculated. Then these three knds of salency results are combned nto an ntensty conspcuty map. C. Feature Integraton Usng the method descrbed n the above secton, conspcuty maps of ntensty feature, color feature and orentaton feature of nput mage are generated. Then we need to ntegrate these feature conspcuty maps nto an ntegrated salency map. In ths paper, a novel and reasonable feature ntegraton strategy s used to combne these feature conspcuty maps nto a fnal salency map. The strategy and process of feature ntegraton are descrbed as follows. We use salent area, salent pont locaton and salent pont dstrbuton to measure the ) Salent ponts extracton: Before combnng feature conspcuty maps nto an ntegrated salency map, we should extract salent ponts [4]. Smply threshold these feature salency maps usng a threshold T whch can be computed usng (8) [8]. t L 2 t + T arg max( p *log p p *log p ).(8) t Where, T s the threshold. L s the total gray level of the feature conspcuty map and p s the frequency that the gray value occurs n the feature salency map. The pxels whose values are bgger than the threshold are consdered as salent ponts. 2) Salent area calculaton: Based on the rarty prncple, the more the salent ponts n a feature salency map are, the less useful the feature salency map s. That means f there are so many salent ponts n a feature salency map, these salent ponts are not really salent. Unlke [4] n whch the sze of a convex hull s consdered as salent pont area, we compute the number of salent ponts as the salent pont area usng (9). The experment results show that ths method s smpler and more effectve than the method of [4]. W area. (9) Where, W area means the weght of salent pont area and represents the number of salent ponts. If the area of salent ponts s larger than 70% of the area of the whole mage, the weght of the feature salency map s set to zero. Ths means t s not ncluded when n feature ntegraton. 3) Salent locaton calculaton: People often pay more attenton to the regon near mage center that means the regon near the mage center are more lkely to be a salent regon. So we consder the salent pont locaton as a crteron. Compute the average dstance between the salent ponts and the mage center as the locaton crteron usng (0) ACADEY PUBLISHER

5 JOURAL OF COPUTERS, VOL. 5, O. 7, JULY W locaton Dst( sp, center). (0) Where, W locaton s the weght of salent pont locaton. s the number of salent ponts. sp means each salent pont and center means the center of the mage. Dst means the dstance between two ponts. 4) Salent ponts dstrbuton: If the salent ponts don t cluster together but dstrbute separately n the feature salency map, the feature salency map s not very useful. So we compute the spatal dstrbuton of salent ponts usng () as another crteron. W dstrbuton Dst( sp, centrod). () Where, W dstrbuto n s the weght of salent pont spatal dstrbuton. centrod means the center of the salent ponts. 5) Feature ntegraton strategy: The weght of each feature map can be computed dynamcally usng (2). W f W m W W f Warea f + W locaton + W dstrbuton. (2) Where, W means the weght of each feature conspcuty map and m s the number of feature conspcuty maps. Then the fnal ntegraton salency map can be generated usng (3). S m W C F *. (3) Where, S s the ntegraton salency map and CF means each feature salency map. Fg. 7 shows an example of feature ntegraton. D. Salent Regon Detecton After generatng the ntegraton salency map, we should threshold t to get a bnary mage B usng (4). Here the value of the threshold T can be computed usng (8). S T B. (4) 0 S < T There are some defects n the bnary mage, so we use some morphologcal operatons to mprove the bnary mage. Frstly, f the bnary mage contans some undesred or solated whte pxels whose eght neghbors are all black pxels, the morphologcal operaton s done to removes these solated pxels from the resultng bnary mage. Secondly, eroson operaton and dlaton operaton are done to fll small gaps wthn the whte regon. Input mage Intensty conspcuty Color conspcuty Orentaton conspcuty Salent ponts Salent ponts Salent ponts w w w Integrated salency map Fgure 7. Example of feature ntegraton Fnally, f the salent regon s not small and very smooth, the whte regon n the generated bnary mage wll only center on the boundary of the regon and the nner of the regon s black regon n the bnary mage. So we fll the enclosed black regons to get a whole whte regon whch represents a whole salent regon n the nput mage. Then salent regons can be extracted by addng the bnary mage to the orgnal mage. Fg. 8 shows the process of detectng salent regon. III. EXPERIET RESULTS The proposed approach was extensvely tested wth many natural mages to ensure proper functonng. We have collected many mage sources from mage search engnes and selected more than 00 mages each of whch contans at least a salent regon to test our approach. Ths secton presents expermental results and analyss of the proposed salent regon detecton approach. Salency map Bnary map Input map Fgure 8. Example of salent regon detecton Salent regon 200 ACADEY PUBLISHER

6 06 JOURAL OF COPUTERS, VOL. 5, O. 7, JULY 200 A. Results The proposed salent regon detecton method has been tested on the computer wth Intel Pentum.8 Ghz and 52 memory usng more than 00 natural scene mages. Fg. 9 shows the smulaton results of the proposed method. Prmary mages are shown n column, salency maps are shown at column 2, bnary mages are presented at column 3 and the salent regons are shown at column 4. proposed method takes local salency, global salency and rarty salency nto account, the defects mentoned above can be elmnated effectvely. Usng the salency maps generated by our methods, the whole salent regons wth correct boundares can be detected. Input mage Itt s results Hou s results Achanta s results Our results Fgure 0. Results of comparson Fgure 9. Results of our proposed method B. Comparson The detecton results of salent regons from our proposed approach are compared wth results from Itt s method [], Hou s spectral resdual method [6] and Achanta s results [3] for the same nput mages. The comparson results are shown n Fg. 0. The nput mages are shown n column. The salency maps generated by Itt s method are shown n column 2. Images n column 3 and column 4 are results of Hou s method and Achanta s method respectvely. Our salency maps are shown n column 5. The salency maps of Itt s model are generated usng salency toolbox whch s downloaded from Salency maps of Hou s are generated usng hs program from s global salency maps are generated usng the program from SourceCode/. From Fg. 0 we can see that Itt s salency maps can only gve the rough locaton of salent regons and they cannot generate correct boundares of salent regons (the second column). In some cases, Hou s and Achanta s methods can get the outlne of salent regons. But n some mages, the salent results computed by ther methods are ncorrect. The edges between objects and background whch have hgh feature contrast wll have hgh salency value but the true salent areas whch are flat wll have low salency value. Ths can be seen especally n the frst row and the thrd row. Because our C. Evaluaton In order to obtan an objectve evaluaton, we also compare our detecton results wth ground truth mages. A huge ground-truth mage database based on boundng boxes has been establshed n [0]. But such boundngbox-based ground truth s far from accurate. Two accurate contour-based ground truth databases have been generated manually n [7] and [3]. In ths paper, we choose the object-contour-based ground truth mage database whch has 000 bnary mages from [3]. Fg. shows some examples of comparson between our segmented bnary mages and ground truth mages. The frst column shows nput mages and the second column shows salency maps generated by our methods. Bnary mages by segmentng salency maps are shown n column 3. Ground truth mages are shown n column 4. In order to gve a quanttatve evaluaton, we use two measurements: ht rate and false alarm rate. Ht rate means the percentage of correct salent ponts whch are consdered salent both by the proposed method and ground truth. False alarm rate means the percentage of false salent ponts whch are consdered salent by the proposed method but refused by ground truth. The ht rate s defned as ht rate x G( x) * B ( x) G( x) x. (5) Where, G s the ground truth mage, B s the segmented bnary mage by the proposed method. The false alarm rate s defned as 200 ACADEY PUBLISHER

7 JOURAL OF COPUTERS, VOL. 5, O. 7, JULY false alarm rate x ( G( x)) * B ( x) x ( G( x)). (6) Where, G s the ground truth mage, B s the segmented bnary mage by the proposed method. Input mage Salency map Bnary mage Ground truth Fgure. Comparson results wth ground truth Tab. shows comparson of average ht rate and average false alarm rate between our method and other methods. From Tab. we can see that Itt s method have the lowest false alarm rate but ts ht rate s the lowest too. Because t salency map can only generate the center locaton of salent regons. Hou s method and Achanta s method both have hgher ht rate and false alarm rate than Itt s method. And they can detect salent regons wth rough borders. But n some mages the detected salent regons ncludes some wrong areas and background and ths ncrease the false alarm rate. False alarm rate of our method s lower than Hou s and Achanta s and smlar wth Itt s. The ht rate of our method s hghest. TABLE I. EVALUATIO RESULT OF DIFFERET ETHODS ethod Average ht rate Average false alarm rate Itt s Hou s Achanta s Ours D. Dscusson If the nput mage s very complex and clustered or there s no very salent regon n the mage, our proposed method wll fal to extract salent regons. Therefore, only those mages whch at least have one salent regon are chosen n our experments. The proposed experment results have shown that our proposed method has performed well n most cases. ore than 85% results have successfully extracted salent regons from the background and matched the ground truth mages. However, our method has some lmtatons ncludng two categores: ot the whole salent regons can be detected n some mages. Some parts of the true salent regons are neglected. Ths leads to low ht rate. Parts of the clustered background are extracted as salent regons. Ths leads to hgh false alarm rate. There are two reasons that are lkely to lead to above defects. One reason s the generated salency map. Although our method has solved some problems of current other methods, the generated salency maps are not very well n some mages whch have very clustered background or low-contrast foreground. Ths need to be further researched. The other reason s the threshold to bnary salency maps. The threshold drectly affects the segmentaton results. If the threshold s too hgh not all the pxels of salent regons wll be extracted. Ths wll result n low ht rate. Contrarly, a low threshold wll lead to too many pxels be extracted. Ths wll result n hgh false alarm rate. We can make a balance between hgh ht rate and low false alarm rate to select the threshold. IV. COCLUSIO A new method for salent regon detecton s proposed n ths paper. ultple features lke ntensty, color and orentaton are analyzed. It computes local salency, global salency and rarty salency to construct the salency map and accordng to the salency map to extract salent regons. The process s entrely unsupervsed. It does not need user nterventon. The proposed model s not doman-specfc and does not mpose lmts on the varety of mages. It can be used for all knd of mages provded that there s at least one meanngful salent regon. Some experment results and quanttatve and qualtatve analyss have been presented n ths paper. The lmtatons of the proposed method are related to the only bottom-up vsual attenton salency map and the threshold to get the bnary mage. The research on topdown vsual attenton to mprove the salency map wll be ncluded n future work. Early vson features are also mportant to construct the salency map. A smple feature can not entrely represent the character of the salent regon. Therefore, multple features analyss s used n the proposed method. In ths paper, we consder colors, ntensty and orentatons as the features of the mage. However, t s very lkely that there are some other features such as edge and symmetry feature whch also should be consdered. What feature and how many features should be extracted accordng to the target wll also be ncluded n future work. ACKOWLEDGET Ths work was supported n part by a grant from atonal atural Scence Foundaton of Chna (o ). 200 ACADEY PUBLISHER

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S usstrunk, Salent regon detecton and segmentaton, Internatonal Conference on Computer Vson Systems, [4] Yqun Hu_, Xng Xe, and We-Yng a, Salent Regon Detecton Usng Weghted Feature aps Based on the Human Vsual Attenton odel, LCS 3332, 2004, pp [5] Xuele, and Xaomng Huo, Statstcal Interpretaton of the Importance of Phase Informaton n Sgnal and Image Reconstructon, Elsever Scence, June [6] Peter J. Bex, and Walter akous, Spatal frequency, phase, and the contrast of natural mages, Journal of Optcal Socety, Amercan, vol. 9, pp , June [7] Byoung Chul Ko, and Jae-Yeal am, Object-of-nterest mage segmentaton based on human attenton and semantc regon clusterng, Journal of Optcal Socety, Amercan, vol. 23, pp , October [8] ZHAG Yong-lang, WAG Yang, LU Huan-zhang, Block objects detecton based on entropy of brghtness, Systems Engneerng and Electroncs, vol. 33, pp , February Qaorong Zhang was born n Zhengzho Chna, n ov 978. She receved her BS degree n 999 and aster degree n 2002 n computer applcaton technology both from Harbn Engneerng Unversty, Harbn Chna. Currently she s a Ph.D. student wth the College of Computer Scence and Technology at Harbn Engneerng Unversty, Harbn, Helongjang, Chna. She s also a lecture at College of Computer and Informaton Engneerng at Henan Unversty of Fnance and Economcs, Zhengzho Henan, Chna. s. Zhang s research nterests and publcatons have been focused on mage processng, artfcal ntellgence and bologcally nspred computng. Habo Lu was born n Harbn, Helongjang, Chna, n 976. He receved hs BS degree n 998, aster degree n 200 and Ph.D. degree n 2005, n computer applcaton technology from Harbn Engneerng Unversty, Harbn Chna. He s an Assocate Professor n the College of Computer Scence and Technology at Harbn Engneerng Unversty, Harbn, Helongjang, Chna currently. Dr. Lu s a member of Chna Computer Federaton. Hs research nterests and publcatons have been focused on artfcal ntellgence, ntellgent robots and computer vson. Jng Shen was born n Helongjang, Chna, n 969. She receved her BS degree n 990 and aster degree n 996 n computer applcaton technology from ortheast Danl Unversty, Jln, Chna. In 2006 she receved her Ph.D. degree n computer applcaton technology from Harbn Engneerng Unversty, Harbn Chna. She s an Assocate Professor n the College of Computer Scence and Technology at Harbn Engneerng Unversty, Harbn, Helongjang, Chna currently. Dr. Shen s a member of Chna Computer Federaton and her research nterests and publcatons have been focused on artfcal ntellgence, ntellgent robots and computer vson. Guochang Gu was born n Shangha, Chna, n Aprl 946. He receved hs BS degree n 967 n computer technology from Harbn Insttute of ltary and Engneerng, Harbn, Chna. He receved hs Ph.D. degree n robot control technology from Courer Unversty, French n 987. He s a Professor n the College of Computer Scence and Technology at Harbn Engneerng Unversty, Harbn, Helongjang, Chna. Professor Gu s a member of Chnese Assocaton of Artfcal Intellgence. Hs research nterests and publcatons have been focused on artfcal ntellgence, ntellgent robots, mage processng and embedded systems. Humn Xao was born n Henan, Chna, n August 963. He receved hs.s. degree n control theory from Huazhong ormal Unversty, Wuhan, Hube, Chna n 988, and Ph.D. degree n automaton theory and technology from South Chna Unversty of Technology, Guangzho Guangdong, Chna n 99. Currently he s a Professor n the College of Informaton at Henan Fnance and Economcs Unversty, Zhengzho Henan, Chna. Professor Xao s research nterests and publcatons have been focused on artfcal ntellgence, ntellgent control and automaton. 200 ACADEY PUBLISHER