Quad Binary Pattern and Its Application in Mean-Shift Tracking

Neurocomputing 217 (2016) 3–10

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Neurocomputing

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Quad binary pattern and its application in mean-shift tracking

Huanqiang Zeng a, Jing Chen a,n, Xiaolin Cui a, Canhui Cai a, Kai-Kuang Ma b a School of Information Science and Engineering, Huaqiao University, Xiamen, China b School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore article info abstract

Article history: This paper proposes a new local texture descriptor, called quad binary pattern (QBP). Compared with local Received 1 September 2015 binary pattern (LBP), the QBP is with stronger robustness for feature extraction under complex scene (e.g., Received in revised form luminance change, similar target and background color) and with lower computational complexity. To 17 November 2015 demonstrate its effectiveness, the proposed QBP is further applied on the mean-shift tracking, in which a Accepted 18 November 2015 joint color-QBP model is developed to effectively represent the color and texture characteristics of the Available online 10 June 2016 target region. Extensive simulation results have demonstrated that the proposed algorithm is able to Keywords: improve the tracking speed and accuracy, compared with the standard mean-shift tracking and joint Target tracking color-LBP model based mean-shift tracking. Quad binary pattern & 2016 Elsevier B.V. All rights reserved. Mean-shift tracking Joint color-QBP model Feature extraction

1. Introduction improved LBP, in which the LBP features are adaptively defined according to the scale and rotation changes. Kim et al. [17] pre- Visual tracking plays an important role and has extensive ap- sented a structural binary pattern that takes both the binarized plications in the field of computer vision, such as, video surveil- differences between the average intensity and the spatial config- lance, human–computer interaction, to name a few [1,2]. In many uration of subregions within the target region into account. Due to visual tracking approaches, the color histogram is broadly served many merits inherited in LBP (e.g., simplicity, robustness to illu- as the appearance model of the target [3–8], since it is robust to mination changes), LBP has been utilized in many areas [18–23], partial occlusions and is independent of scale and rotation. Among such as, pedestrian detection [18,19], facial expression recognition them, the mean-shift tracking [9,10] is the most widely-used one [20], tea leaves classification [21], image retrieval [22], medical because of its simplicity and high efficiency, which works by field [23], and so on. In the mean-shift tracking, similar to the color searching the location of a region in each frame that is most si- model, the LBP can also serve as an estimation model of pixel milar to the target region. However, the mean-shift tracking using sample distribution, and well cooperate with color model to re- fi only color feature may fail in the situation that the background present the target features. Ning et al. [24] rstly incorporated LBP and the target have similar color or there is illumination change, into mean-shift tracking, in which a joint color-LBP model was which will be often encountered in the real word. introduced to represent the target. This approach is able to en- It is known that different kinds of features have been in- hance the robustness of mean-shift tracking when the input image contains target-color like background, but at the expense of troduced to achieve a better object representation and classifica- slowing down the tracking speed. Hence, some works have been tion in various domains (e.g., [11–13]). Among them, texture fea- developed to improve the performance of joint color-LBP model tures, which reflect the spatial structure of the target that color based mean-shift tracking (e.g., [25,26]). In [25], LBP was firstly feature does not describe, have been developed and applied in the shaped into a rotation invariant permutation group (PG) to reduce visual tracking. Local binary pattern (LBP) that is originally pre- its computational load and enhance its anti-target rotation ability. sented for texture classification [14] is one of the most widely- Then, a jointcolor-PGLBP model was developed and incorporated used texture features. Note that many improved variations of LBP into the continuous adaptive mean-shift (Camshift) tracking algo- have been made. For example, Qian et al. [15] developed an ex- rithm to improve the tracking ability under the complicated tended LBP in pyramid transform domain by considering the tex- background. To improve the tracking speed, a similar operator, ture resolution variations. Davarzani et al. [16] proposed an called local similarity number (LSN), was introduced to extract textural information [26]. The mean-shift tracking using joint n fi Corresponding author. color-LSN model can improve the tracking ef ciency but decline E-mail address: [email protected] (J. Chen). its target discerning ability. http://dx.doi.org/10.1016/j.neucom.2015.11.130 0925-2312/& 2016 Elsevier B.V. All rights reserved. 4 H. Zeng et al. / Neurocomputing 217 (2016) 3–10

In order to get a more accurate and efficient tracking perfor- ULBP()=|(−)−(−)|PR, sgPc−10 g sg g c mance, this paper presented a new texture feature, called quad P−1 binary pattern (QBP). The QBP is computed based on 2 × 2 block, +|(−)−(−)|∑ sgpc g sg p−1 g c using the average mean of these four pixels as its threshold. p=1 ()4 Compared with the classical LBP, the proposed QBP is more robust On this basis, the patterns of LBP are merged, and the number of under the complex conditions and has lower computational patterns is therefore dramatically reduced. Take LBP8,1 as an ex- complexity. To demonstrate the usefulness of QBP in target ample, its 256 patterns are merged into 36 patterns [27]. Fig. 2 tracking, a joint color-QBP model is developed and incorporated illustrates nine typical uniform patterns of LBP8,1. into mean-shift tracking. Experimental results clearly show that Although LBP is calculated based on the difference between the the proposed approach is able to greatly improve the tracking pixel values of the center point and its neighborhoods, and can accuracy and speed, compared with the classical mean-shift reflect the texture information of the gray-scale image, it ignores tracking and the joint color-LBP based mean-shift tracking. the pixel value of the center point to some extent, which may The remaining of this paper is organized as follows. The LBP is contain some important structural information. For instance, two briefly reviewed and the proposed QBP is presented in Section 2.A pixels with quite different gray-scale values may have the same mean-shift tracking based on the proposed joint color-QBP model LBP codes, as shown in Fig. 3(a), which may decline the dis- fi is introduced in Section 3. The simulation results are provided and criminative ability of LBP for texture classi cation. Moreover, the discussed in Section 4. Finally, the conclusion is given in Section 5. computational complexity of LBP still cannot meet the require- ments of real-time applications. Therefore, developing a new feature descriptor with better discriminative ability and lower computational load is desirable. 2. Quad binary pattern 2.2. Quad binary pattern (QBP) 2.1. Brief review of local binary pattern (LBP) Based on the above analysis, we develop a new texture de- LBP is a gray-scale invariant operator, which was originally scriptor, namely, quad binary pattern (QBP), in this paper. Different proposed by Ojala et al. [14] to summarize the local gray-level from LBP, QBP is defined on a quad of pixels (2 ×2 block), including structure. The LBP operator is defined as follows: the current pixel, its left pixel, its upper pixel, and its left upper pixel, as shown in Fig. 4. The QBP operator is defined as follows: 7 p 3 LBP()=·() xc,2, yc ∑ s gpc g p p=0 ()1 QBP=·()∑ 2, s gp Mean , t p=0 ()5 where gc is the gray value of the center pixel (xyc, c ), and gp is the p-th pixel of its 8 neighborhoods. The indicator s (·) is defined as: where gp represents the pixels in the 2 × 2 block, Mean is the arithmetic mean of this block and computed as: ⎧ ⎪10ggpc−≥ sg()=, g ⎨ 1 3 pc ⎩⎪ 00gg−< Mean= ∑ g pc ()2 4 p p=0 ()6 An example of the coding procedure of the LBP is shown in and t is the threshold of smooth gray fluctuations. The indicator Fig. 1. The LBP was then extended to circular neighborhood LBP s (·) is defined as operator, LBPPR, , by using circular neighborhoods associated with ⎧ bilinear interpolation, in which P denotes the neighborhood ⎪1 gMeantp −≥ sg()=,, Meant ⎨ number, and R denotes the radius of circular neighborhoods [27]. p ⎪ ⎩ 0 gMeantp −< ()7 The rotation invariant version of LBPPR, is able to be derived by supplementing with a rotation invariant measure of local contrast. An example of the coding procedure of QBP is shown in Fig. 4. One commonly-used rotation invariant version of the Uniform The QBP contains 16 patterns, which can be synthesized into

Pattern, LBPPR, ,isdeﬁned as: 6 classes according to the rotation invariance criterion, as shown in Fig. 5. ⎧ P−1 ⎪ Compared with the LBP, the QBP can better describe the local ∑ sg(−) gif ULBP (PR, )≤ 2 LBP = ⎨ pc texture characteristics of the image and has lower computational PR, ⎪ p=0 complexity. For instance, as shown in Fig. 3(b), QBP has better ⎩ P + 1else ()3 discriminative ability compared with LBP, since two pixels with where quite different gray-scale values have different QBP codes while

Fig. 1. Procedure of the LBP coding and its representation. H. Zeng et al. / Neurocomputing 217 (2016) 3–10 5

Fig. 2. Nine uniform patterns of LBP8,1.

Fig. 3. Comparison of LBP and QBP of two different local textures with the same pixel of center point. The LBP codes are same, while the QBP codes are different.

Fig. 4. Procedure of the QBP coding and its representation. have the same LBP codes. To further demonstrate its effectiveness, the QBP is applied on the mean-shift tracking described in the next section.

3. Mean-shift tracking using joint color-QBP model

3.1. Mean-shift tracking

It is known that mean-shift tracking is realized by computing the likelihood of target model and its candidate models, which are defined as an ellipsoidal or rectangular region and represented by color histogram [9,10]. First of all, the target model q^ in mean-shift tracking is defined as: ^ ^ q ={qu}um=…1, , ()8 ^ Fig. 5. Six rotation invariant pattern classes of QBP. where m represents the number of feature spaces, and qu means the probability of feature (i.e., u =…1, , m) in the target model, which can be calculated as: feature at location x, δ is the Kronecker delta function, k(x)isan fi n isotropic kernel pro le that assigns larger weights to the closer ^ ⁎⁎2 pixels to the circle center. Note that the kernel is normalized to qkxbxuu =(∥∥)[()−]∑ iiδ n ⁎ i=1 ()9 fi 2 make its pro le satisfy ∑i=1 kx∥∥=i 1 by imposing the condi- m ⁎ tion ∑ q^ = 1. where xi denotes the normalized pixel locations in the target u=1 u region, b(x) is the bin number (i.e., 1,,… m) in relation to the In a similar manner, the target candidate model p^ (y) can be 6 H. Zeng et al. / Neurocomputing 217 (2016) 3–10

defined as: the texture and a joint color-QBP model is then developed. More specifically, in the joint color-LBP model, each color channel in p^ ()={ypy^ ()} u um=…1, , ()10 RGB color space was divided into 8 bins. Combining these color ^ bins with the five major uniform patterns of LBP, an 8 ×××885 where pu (y) means the probability of the feature (i.e., u =…1, , m) in the target candidate model, that is: joint color-LBP histogram was established as the target model. This joint color-LBP model can make the face tracking algorithm more nh ⎛ 2 ⎞ ^ ⎜ yx− i ⎟ robust under skin-color like background, but meanwhile causing py()=∑ k⎜ ⎟δ [(bxi )−] u u ⎝ h ⎠ higher computational complexity. i=1 ()11 Motivated by this point, we employ the proposed QBP to ex-

where {xii} =…1, , nh denotes the normalized locations of the pixels in tract the textural information of the target instead of LBP, as the a target candidate region centered at y, nh represents the total QBP has better texture description ability and lower computational pixels in the target region, and h is the bandwidth of the kernel k complexity. Then, a joint color-QBP model is designed to represent (x). Note that the kernel is normalized to make its profile meet the target and is then incorporated into the mean-shift tracking to yx− ∑nh k ∥∥=i 2 1 by imposing the condition ∑m p^ = 1. further improve the tracking performance. Unlike the joint color- i=1 h u=1 u Moreover, the similarity between the target model and its LBP model, for color feature, the proposed joint color-QBP model candidate model can be measured based on Bhattacharyya coef- uses H-component in HSV color space and quantizes it into 16 ficient: bins, since H-component is more robust to luminance fluctuation

m [29]. For texture feature, the four patterns (i.e., Patterns 1, 2, 3 and ^ ^ ^ ^ ρρ()=yy (pq(), )= ∑ pyquu() 4) of the QBP features computed by (5) are exploited to represent u=1 ()12 the texture information of the image regions. Hence, the proposed mean-shift tracking using joint color-QBP model computes the The goal of mean-shift tracking is to ﬁnd the location of the target model using (8), where the number of histograms is equal most similar target candidate in the current frame corresponding to u =×16 4. Similarly, the target candidate model can be com- to the target, hence, their similarity (12) should be maximized as a ^ function of y. For that, the linear approximation of the Bhatta- puted by (10). Let q be the target model and y0 be its location in charyya coefﬁcient is obtained by using the Taylor series expan- the previous frame, the proposed approach can be described as ^ follows: sion around the target candidate pu (y0 ) at the location y0. m m ^ (1) Calculate the current target candidate model p^ (y ) by (10), and 1 ^ ^ 1 ^ qu 0 ρ()≃ypyqpy∑∑uu()0 + u () ^ ^ 2 2 ^ compute the similarity ρ(pq()y0 , ) by (12). u==1 u 10pyu () ()13 (2) Calculate the pixel weights wi by (15). By substituting p^ (y) in (11) into Eq. (13), it can get (3) Calculate the new candidate location y1 by (16) u ^ (4) Calculate the new target candidate model p(y1) by (10), and m nh ⎛ 2 ⎞ ^ ^ 1 ^ ^ 1 ⎜ yx− i ⎟ compute the similarity ρ(pq()y1 , ) by (12). ρ()≃ypyqwk∑∑() + i ⎜ ⎟ 1 2 uu0 2 ⎝ h ⎠ (5) If ρ(pq^ ()y , ^) is smaller than ρ(pq^ ()y , ^), set yyy←( + ), and u==1 i 1 ()14 1 0 1 2 01 go to Step 4.

where (6) If ∥ yy10−∥<ε or the number of iterations reached Nmax, stop (where ε = 1 and Nmax = 10 in this work). Otherwise, set m q^ w = u δ [(bx )−] u yy01← , and go to Step 2. i ∑ ^ i u=10pyu () ()15

Considering the first term of Eq. (14) is independent of y, max- 4. Experimental results and discussion imizing the similarity (12) is equal to maximize the second term of Eq. (14), which represents the density estimation computed with In this work, extensive experiments have been conducted to kernel profile k(x) in terms of y in the current frame. In the mean- evaluate the performance of the proposed QBP and its application shift approach, the kernel is recursively moved from the current in mean-shift tracking. The experimental platform is the computer location y to the new location y based on the following re- 0 1 with OS–WINDOWS 7, CPU—Intel i5 processor, memory size— lationship [28]: 4GB, and Visual Studio 2010/Matlab 2011a. ⎛ 2 ⎞ Firstly, we compared the textural extraction capacity of the nh yx0 − i ∑ wgi ⎜ ⎟xi i=1 ⎝ h ⎠ proposed QBP with the LBP from the following aspects: the ro- y = 1 ⎛ 2 ⎞ bustness to illumination conditions, the scale invariant and the nh yx0 − i ∑ wgi ⎜ ⎟ rotation invariant, where the threshold t¼4 is used for QBP i=1 ⎝ h ⎠ ()16 computation. To evaluate the robustness to illumination condi- where g(x) is the negative derivative of the kernel profile, i.e., tions, Fig. 6 illustrates the textual extraction results on two facial −kx′( ). Based on Eq. (16), the mean-shift tracking method can find images under different illumination conditions, where the original the target candidate region in each frame that is most similar to image, textures extracted by LBP and textures extracted by QBP are the target. shown from top to bottom, respectively. Table 1 further shows the textural similarity between the original image and the textures 3.2. Mean-shift tracking using joint color-QBP model extracted by LBP and QBP in terms of Bhattacharyya coefficient under different illumination conditions as shown in Fig. 6. One can As mentioned in Section 1, mean-shift tracking using only color see that both LBP and QBP are somewhat robust to lighting fluc- histogram may fail when the tracking window is attracted by a tuation and the proposed QBP is able to achieve higher textural target-color like background or under illumination change. To al- similarity, which means that the QBP is more robust than LBP leviate the influences of such situations, Ning et al. [24] utilized under complex luminance conditions. Furthermore, we tested the the joint color-texture histogram to represent the target instead of LBP and the proposed QBP on an image (i.e., Fig. 7) as an example using only color histogram, in which the LBP is exploited to extract to evaluate their ability on scale invariant and rotation invariant. In H. Zeng et al. / Neurocomputing 217 (2016) 3–10 7

Fig. 6. An illustration of textural extraction capacity of LBP and QBP on the facial images under different luminance conditions. From top to bottom, the original image, textures extracted by LBP and textures extracted by QBP are displayed.

Table 1 Table 3 A comparison of textural similarity resulted from the LBP and proposed QBP A comparison of textural similarity resulted from the LBP and proposed QBP measured in terms of Bhattacharyya coefﬁcient under various illumination measured in terms of Bhattacharyya coefﬁcient under various rotation angles. conditions. Texture descriptor/rotation angle 10° 20° 30° 40° 50° Texture Images Normal Uneven High Low descriptor luminance luminance luminance luminance LBP 0.9841 0.9857 0.9829 0.9866 0.9794 QBP 0.9919 0.9862 0.9852 0.9881 0.9798 LBP Fig. 6(a) 1 0.9885 0.9841 0.9599 QBP 1 0.9958 0.9914 0.9776 Texture descriptor/rotation angle 60° 70° 80° 90° LBP Fig. 6(b) 1 0.9809 0.9476 0.9877 QBP 1 0.9938 0.9934 0.9944 LBP 0.9619 0.9505 0.9389 0.9595 QBP 0.9775 0.9811 0.9821 0.9835

our experiments, the test image is transformed to another image by enlarging/shrinking in a certain scale or rotating in a certain angle, and the textural similarity between the original image and the textures extracted by the LBP and proposed QBP is computed in terms of Bhattacharyya coefﬁcient. The corresponding results under various scales and rotation angles are shown in Table 2 and 3, respectively. It can be seen that compared with LBP, the proposed QBP is able to achieve higher textural similarity, which means that the QBP has better ability on scale invariant and rotation invariant. Secondly, we tested the performance of the proposed joint color-QBP model based mean-shift tracking and compared it with the standard mean-shift tracking based on RGB model [9], the improved mean-shift tracing using the joint color-LBP model [24]. To have a fair comparison, the original source codes of the existing methods [9,24] provided by the authors with tuned parameter are used. Fig. 8 shows the tracking results produced by the mean-shift Fig. 7. Test image. tracking based on RGB model [9], the joint color-LBP model [24], and the proposed joint color-QBP model on a self-taken sequence, “MyVideo1”. It can be observed from Fig. 8 that the mean-shift Table 2 tracing based on RGB model [9] is prone to be attracted by the A comparison of textural similarity resulted from the LBP and proposed QBP measured in terms of Bhattacharyya coefﬁcient under various scales. skin-color like interference (e.g., attracted by the neck in 107th frame), while both the joint color-LBP model [24] and the pro- Texture descriptor/scale 0.25 0.5 2.0 4.0 posed color-QBP model achieve a robust tracking performance. Moreover, Fig. 9 shows the tracking results produced by the mean- LBP 0.9896 0.9965 0.9480 0.8809 shift tracking algorithm based on RGB model [9], the joint color- QBP 0.9910 0.9972 0.9621 0.90001 LBP model [24], and the proposed joint color-QBP model on 8 H. Zeng et al. / Neurocomputing 217 (2016) 3–10

Fig. 8. Tracking results on 1st, 99th, 107th, and 190th frame (from left to right) of Sequence “MyVideo1”. From top to bottom, the images show the results by mean-shift tracking based on RGB model [9], the joint color-LBP model [24], and the proposed joint color-QBP model, respectively.

Fig. 9. Tracking results on 1st, 39th, 124th, 137th, 155th and 175th frame (from left to right) of Sequence “MyVideo2”. From top to bottom, the images show the results by mean-shift tracking based on RGB model [9], the joint color-LBP model [24], and the proposed joint color-QBP model, respectively. another sequence under complex background, “MyVideo2”. From and its 8 neighborhood, while the proposed QBP only needs to Fig. 9, one can see that the mean-shift tracking based on RGB calculate the difference between the 4 pixels and their arithmetic model [9] is more sensitive to illuminance ﬂuctuation and be- mean, which can be obtained through three additions and one comes invalid when the illumination is low, leading to the failure shift operation. For the length of binary codes, the LBP code is 8 bit of tracking (i.e., frames after 124th frame). Joint color-LBP model while proposed QBP is 4 bit, which is half of LBP. For the number of [24] improves the tracking performance to some extent, which can texture feature modes, QBP is also less than LBP, which guarantees be seen on the 124th and 137th frame, but the error accumulation that the mean-shift tracking using joint color-QBP model has eventually leads to the failure (e.g., 155th frame). In contrast, the lower computational complexity. On the other hand, in terms of proposed mean-shift using joint color-QBP method keeps robust histograms, the size of RGB model [9] is 1616164096××= , that tracking. This is because (1) it uses H-component that is robust to of joint color-LBP model [24] is 8 ×××=8852560, but the luminance ﬂuctuation to model the color feature; and (2) the proposed color-QBP model only needs 16×= 4 64 bins. Table 4 proposed QBP is more robust in complex illumination conditions. documents the tracking speed comparison among mean-shift In addition, for computational complexity, on one hand, the tracking based on RGB model [9], joint color-LBP model [24], and original LBP needs to calculate the difference between center point the proposed algorithm on sequence “MyVideo1”. It can be seen H. Zeng et al. / Neurocomputing 217 (2016) 3–10 9

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Huanqiang Zeng received the B.S. and M.S. degrees References from Huaqiao University, Xiamen, China and the Ph.D. degree from Nanyang Technological University, Singa- pore, all in electrical engineering. He is currently a [1] A.W. Smeulders, D.M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, M. Shah, professor at the School of Information Science and Visual tracking: an experimental survey, IEEE Trans. Pattern Anal. Mach. Intell. Engineering, Huaqiao University, Xiamen, China. His 36 (7) (2014) 1442–1468. research interests are in the areas of visual information [2] Y. Gao, R.R. Ji, L. Zhang, A. Hauptmann, Symbiotic tracker ensemble towards a processing and computer vision. uniﬁed tracking framework, IEEE Trans. Circuits Syst. Video Technol. 24 (7) (2014) 1122–1131. [3] P. Pérez, C. Hue, J. Vermaak, M. Gangnet, Color-based probabilistic tracking, in: European Conference on Computer Vision, 2002, pp. 661–675. [4] N.S. Peng, J. Yang, Z. Liu, Mean shift blob tracking with kernel histogram ﬁl- tering and hypothesis testing, Pattern Recognit. Lett. 26 (5) (2005) 605–614. [5] P. Li, An adaptive binning color model for mean shift tracking, IEEE Trans. Circuits Syst. Video Technol. 18 (9) (2008) 1293–1299. [6] I. Leichter, M. Lindenbaum, E. Rivlin, Mean shift tracking with multiple re- Jing Chen received B.S. and M.S. degrees from Huaqiao ference color histograms, Comput. Vis. Image Underst. 114 (3) (2010) 400–408. University, China and the Ph.D. degree from Xiamen [7] J. Ning, L. Zhang, D. Zhang, C. Wu, Robust mean-shift tracking with corrected University, China. She is now an associate professor at background-weighted histogram, IET Comput. Vis. 6 (1) (2012) 62–69. the School of Information Science and Engineering, [8] Z. Hou, X. Liu, W. Yu, W. Li, A. Huang, Mean-shift tracking algorithm with Huaqiao University, Xiamen, China. Her current re- improved background-weighted histogram, in: 2014 Fifth International Con- search interests include image processing and compu- ference on Intelligent Systems Design and Engineering Applications (ISDEA), ter vision. 2014, pp. 597–602. [9] D. Comaniciu, V. Ramesh, P. Meer, Real-time tracking of non-rigid objects using mean shift, in: IEEE Conference on Computer Vision and Pattern Re- cognition, vol. 2, 2000, pp. 142–149. [10] D. Comaniciu, V. Ramesh, P. Meer, Kernel-based object tracking, IEEE Trans. Pattern Anal. Mach. Intell. 25 (5) (2003) 564–577. 10 H. Zeng et al. / Neurocomputing 217 (2016) 3–10

Xiaolin Cui received the B.S. and M.S. degrees from development. His research interests are in the areas of digital image processing and Huaqiao University, Xiamen, China. She is pursuing computer vision. master degree at the School of Information Science and Dr. Ma has produced extensive publications in various international journals, Engineering, Huaqiao University. Her research interests conferences, and MPEG standardization meetings. He holds one U.S. patent on fast are object tracking and machine learning. motion estimation algorithm. He is a Distinguished Lecturer in the IEEE Circuits and Systems Society from 2008 to 2009. He has been actively contributing various technical services for numerous international conferences. He is a General Co-Chair of the Visual Communications and Image Processing 2013, and he served as a Technical Program Co-Chair in multiple international conferences, including the IEEE International Conference on Image Processing in 2004, the International Symposium on Intelligent Signal Processing and Communication Systems in 2007, and the Pa- ciﬁc-Rim Symposium on Image and Video Technology in 2010. Dr. Ma has served as an Associate Editor of the IEEE Transactions on Image Processing from 2007 to 2010, the IEEE Transactions on Communications from 1997 to 2012, and the IEEE Trans- actions on Multimedia from 2002 to 2009. He has been serving as an Editorial Board Member of the Journal of Visual Communication and Image Representation since 2005 Canhui Cai received the B.S., M.S., and Ph.D. degrees in and the International Journal of Image and Graphics since 2002. He is an Elected electronic engineering from Xidian University, Xi'an, Member of three IEEE Technical Committees: Image and Multidimensional Signal China, Shanghai University, Shanghai, China, and Tian- Processing Committee from the IEEE Signal Processing Society, the Multimedia jin University, Tianjin, China, in 1982, 1985, and 2003, Communications Committee from the IEEE Communications Society, and the Digital respectively. He has been with the Faculty of Huaqiao Signal Processing from the IEEE Circuits and Systems Society. He served as a Sin- University, Quanzhou, China, since 1984, He was a gapore MPEG Chairman and the Head of Delegation from 1997 to 2001, and the Visiting Professor with Delft University of Technology, Chairman of the IEEE Signal Processing Singapore Chapter from 2000 to 2002. He is a Delft, The Netherlands, from 1992 to 1993, and a Vis- member of Eta Kappa Nu and Sigma Xi. He is an IEEE Fellow. iting Professor with University of California at Santa Barbara, Santa Barbara, CA, USA, from 1999 to 2000. He is currently a Professor with the School of In- formation Science and Engineering, Huaqiao University. He has authored or co-authored four books and pub- lished more than 120 papers in journals and conference proceedings. His research interests include video communications, image and video signal processing and computer vision. Dr. Cai was a General Co-Chair of Intelligent Signal Processing and Commu- nication Systems in 2007. He is an IEEE senior member.

Kai-Kuang Ma received the B.E. degree in electronic engineering from Chung Yuan Christian University, Chung-Li, Taiwan, the M.S. degree from Duke Uni- versity, Durham, NC, USA, and the Ph.D. degree from North Carolina State University, Raleigh, NC, USA, both in electrical engineering. He is currently a Full Professor with the School of Electrical and Electronic Engineering, Nanyang Tech- nological University, Singapore. From 1992 to 1995, he was a Technical Staff Member with the Institute of Microelectronics, Singapore, working on digital video coding and the MPEG standards. From 1984 to 1992, he was with IBM Corporation, Kingston, NY, USA, and Re- search Triangle Park, NC, USA, engaging on various DSP and VLSI advanced product