Inter national J our nal of Electrical and Computer Engineering (IJECE) V ol. 7, No. 5, October 2017, pp. 2565 – 2573 ISSN: 2088-8708 2565       I ns t it u t e  o f  A d v a nce d  Eng ine e r i ng  a nd  S cie nce   w     w     w       i                       l       c       m     V ideo Shot Boundary Detection Using The Scale In v ariant F eatur e T ransf orm and RGB Color Channels Zaynab El khattabi 1 , Y ouness T abii 2 , and Abdelhamid Benkaddour 3 1,3 LIR OSA Laboratory , F aculty of Sciences, Abdelmalek Essaadi Uni v ersity ,T etuan, Morocco 2 LIR OSA Laboratory , National School of Applied Sciences, Abdelmalek Essaadi Uni v ersity , T etuan, Morocco Article Inf o Article history: Recei v ed: May 5, 2017 Re vised: Jun 12, 2017 Accepted: Jun 29, 2017 K eyw ord: V ideo Se gmentation Shot Boundary Detection Gradual T ransition Abrupt Change SIFT ABSTRA CT Se gmentation of the video sequence by detecting shot changes is essential for video analysis, inde xing and retrie v al. In this conte xt, a shot boundary detection algorithm is proposed in this paper based on the scale in v ariant feature transform (SIFT). The first step of our method consists on a top do wn search scheme to detect the locations of tran- sitions by comparing the ratio of matched features e xtracted via SIFT for e v ery RGB channel of video frames. The o v ervie w step pro vides the locations of boundaries. Sec- ondly , a mo ving a v erage calculation is performed to determine the type of transition. The proposed method can be used for detecting gradual transitions and abrupt changes without requiring an y training of the video content in adv ance. Experiments ha v e been conducted on a multi type video database and sho w that this algorithm achie v es well performances. Copyright c ī€ 2017 Institute of Advanced Engineering and Science . All rights r eserved. Corresponding A uthor: Zaynab El khattabi F aculty of Sciences, Abdelmalek Essaadi T etuan, Morocco zaynabelkhattabi@gmail.com 1. INTR ODUCTION The high increasing v olume of video content on the W eb has created profound challenges for de v eloping ef ficient inde xing and search techniques to manage video data. Whereas m anaging multimedia data requires more than collecting the data into storage archi v es and deli v ering it via netw orks to homes or of fices, content based video retrie v al is becoming a highly recommended trend in man y video retrie v al systems. Ho we v er , con v entional techniques such as video compression and summarization stri v e for the tw o commonly conflicting goals of lo w storage and high visual and semantic fidelity [1]. V ideo se gmentation is the fundamental process for a number of applications related to automatic video inde xing, bro wsing and video analysis. The basic requirement of video se gmentation is to partition a video into shots. It is often used as a basic meaningful unit in a video. In [2], Thompson et al. defined a video shot as the smallest unit of visual information captured at one time by a camera that sho ws a certain action or e v ent. Therefore, se gmenting video into separate video shots needs to detect the joining of tw o shots in the video and locate the position of these joins. There are a number of dif ferent types of transitions or boundaries between shots. A cut is an abrupt shot change that occurs in a single frame. A f ade is a slo w change in brightness usually resulting in or starting with a solid black frame. A dissolv e occurs when the images of the first shot get dimmer and the images of the second shot get brighter , with frames within the transition sho wing one image superimposed on the other . A wipe occurs when pix els from the second shot replace those of the first shot in a re gular pat tern such as in a line from the left edge of the frames [3]. Other types o f shot transitions include computer generated ef fects such as morphing. The ef fects of this kind of transition are obtained with the h e lp of the cross-dissolv e or f ading techniques which permit to achie v e a smooth change of image content (i.e. te xture and/or color) from source to tar get frames. Whereas there is a wealth of research on shot boundary detecti o n (SBD), some methods aim at detecting J ournal Homepage: http://iaesjournal.com/online/inde x.php/IJECE       I ns t it u t e  o f  A d v a nce d  Eng ine e r i ng  a nd  S cie nce   w     w     w       i                       l       c       m     DOI:  10.11591/ijece.v7i5.pp2565-2573 Evaluation Warning : The document was created with Spire.PDF for Python.
2566 ISSN: 2088-8708 abrupt boundaries, while others focus on gradual boundaries. In addition, certain kind of transitions can be easily confused with camera motion or object motion. In this paper , a shot boundary detection scheme bas ed on SIFT is proposed. Section 2. presents the v arious methods that ha v e been proposed in thi s field, section 3. presents the method. Finally , section 4. and 5. gi v e the e xperiments and a conclusion. 2. RELA TED W ORKS In literature, Algorithms for shot boundary detection can broadly be classified into man y groups; we can find lots of techniques include comparison of pix el v alues, statistical dif ferences, histogram comparisons, edge dif ferences, compression dif ferences, and motion v ectors to quantify the v ariation of continuous video frames. The easiest w ay to detect if tw o frames are significantly dif ferent is to count the number of pix els that change in v alue more than some threshold. This total is compared ag ainst a second threshold to determine if a shot boundary has been found. Only the luminance channel of the considered videos is considered in this case. If the number of pix els which change from one image to another e xceeds a certain threshold a shot transition is declared [4]. A technique introduced and v alidated during the TRECVID 2004 campaign is presented in [5]. First, small images are created from the original frames by taking one pix el e v ery eight pix els and the y are con v erted to HSV color space, only the V component is k ept for luminance processing. W ith e v ery ne w frame, the absolute dif ference between pix els intensity is computed and compared with the a v erage v alues to detect cut transitions. Re g arding the gradual transitions the method can detect only dissolv es and f ades. The idea proposed in [6] is di viding the images into 12 re gions and founding the bes t match for each re gion in a neighborhood around the re gion in the other image. Gradual t ransitions were detected by generating a cumulati v e dif ference measure from consecuti v e v alues of the image dif ferences.The incon v enient of methods based on comparison of pix el v alues is their sensiti vity to camera motion. T o a v oid this problem of camera motion and object mo v ements, some tec h ni ques can be done by com- paring the histograms of successi v e images. The idea behind histogram-based approaches ( [7], [8]) is that tw o frames with unchanging background and unchanging (although mo ving) objects will ha v e little d i f ference in their histograms. Color histograms are used in [9] to detect shot boundaries by representing each frame of the video by their color histogram features. Then, the video frames are treated as a sequence of feature v ectors which are fed to the split and mer ge frame w ork. After completion of recursi v e split and mer ge process, the s h ot boundaries are identified easily . Another approach to detect shot boundaries is edge/contour -based methods that e xploit the contour in- formation present in the indi vidual frames, under the assumpt ion that the amount and location of edges between consecuti v e frames should not change drastically . In [10], the feature of edge pix el count is proposed for shot detection, where Sobel edge detector is used. Besides, color , edge or te xture information can be combined to mak e use of the adv antages of all this features and increase the accurac y of the technique used. An e xample of this combination is proposed in [11] using global color features combined with the characteristics of local edge. Some temporal filtering mechanism is used to eliminate camera motion noise when it is present in detect- ing shot changes. The w ork analysis resides in the discrimination between camera w ork-induced apparent motion and object motion-induced apparent motion, follo wed by analysis of the camera w ork-induced motion in order to identify camera w ork [12]. In [13], an approach block-based motion estimation is used, in which the whole frame is di vided into possible blocks of 3x3 pix els. All pix els within the same block are assumed to belong to the same object, which under goes translational motion. Each block is compared with all possible such blocks within the corresponding search windo w with the same center pix el location in current frame. In an other side, a camera motion characterization technique is introduced in [14] using a camera motion histogram descriptor to represent the o v erall motion acti vity of a shot. V arious features can be combined to mak e use of the adv antages of v arious popular techniques such as color , te xture, shape and motion v ectors in spatial as well as in transformed domains such as F ourier , cosine w a v elets, Eigen v alues, etc. An e xample of such combinations is presented in [15] where color feature is used and in [16], where te xture feature is used. T e xture methods lik e Local Binary P atterns (LBP) are used in v arious recent computer vision and pattern recognition applications. In [16] an e xtension of LBP histogram is used to represent the frame te xture, it is called Midrange LBP (MRLBP). The authors justify their proposition by the comparison of gray center pix el v alue, a v erage gray v alue and midrange gray v al ue that is more rob ust to noise and illumination v ariants. LBP histogram v alues are e xtracted based on midrange statistics on each frame and the y are stored as a feature v ector in a video sequence. Then, the dissimilarity metric is applied on the feature v ectors of adjacent frames to be used for shot detection process using adapti v e threshold approach. IJECE V ol. 7, No. 5, October 2017: 2565 – 2573 Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE ISSN: 2088-8708 2567 Shot boundary detection approaches can also be cate gorized based on machine learning techniques such as support v ector machines, neural netw orks, fuzzy logic, clustering techniques and Eigen analysis [17] . In this conte xt, the problem of shot detect ion in endoscopic sur gery videos is addressed in [18] to manage the video content of sur gical procedures. The method proposed relies on the application of a v ariational Bayesian (VB) frame w ork for computing the posterior distrib ution of spatiotemporal Gaussian mixture models (GMMs). The video is first decomposed into a series of consecuti v e clips of fix ed duration. Then, the VBGMM algorithm is applied on feature v ectors e xtracted from each clip to handle automatically the number of components which are matched along the video sequence. These components denote clusters of pix els in the video clip with similar feature v alues and the labels are the tags of these components. Hence, the process of label tracking starts to define s hot borders when component tracking f ails, signifying a dif ferent visual appearance of the sur gical scene. Genetic Algorithm and Fuzzy Logic ha v e been also used for shot boundary detection. The authors of [19] proposed a system based on computing the Normalized Color Histogram Dif ference between each tw o consecuti v e frames in a video. Then, a fuzzy system is performed to classify the frames into abrupt and gradual changes. In order to optimize the fuzzy system, genetic algorithm GA is used. The results sho w the benefits of the GA optimization process on achie ving a lo w computational time. Man y recent approaches reported in the literature related to shot boundary detection rely on SIFT ([20], [21]). The method proposed in [20] i s based on SIFT -point distrib ution histogram e xtraction. Each video frame is represented by a histogram, named SIFT -point distrib ution histogram (SIFT -PDH). It describes the distrib ution of the e xtracted stable k e ypoints within the frame under polar coordinates. Distance comparison represents the dif ference between each tw o consecuti v e frames of the video; it is calculated by comparing their SIFT -PDHs. An adapti v e threshold is used to identify the shot boundaries. Some other surv e ys of e xisting SBD techniques in the literature are pro vided and discussed in [22]. 3. PR OPOSED METHOD Selection of an appropriate approach feature for se gmenting a video sequence into shots is the most critical issues. Se v eral such features ha v e been suggest ed in the literature (histogram dif ference, optical flo w ...), b ut none of them is general enough to operate for all of changes in the video data. The proposed method is based on feature e xtraction using scale in v ariant feature transform adopted by Da vid G. Lo we [23]. The reason of this choice is that the SIFT image features are in v ariant to image rotation, scale and rob ust across a substantial range of af fine distortion, addition of noise, and change in illumination. Firstly , the video is o v ervie wed and zooms in where v er a shot boundary e xists using a top do wn search scheme that is presented in [24]. The search is carried out by comparing the ratio of matched k e ypoints e xtracted via SIFT for e v ery RGB channel of tw o video frames separated by a temporal sampling period N . SIFT descriptors are computed o v er all three channels of the RGB color space. Hence, three feature descriptors matrices associated with R, G and B color spaces ha v e been obtained for each N th frame. Instead of comparing the number of SIFT feature k e y point s, we calculate and compare the ratio of matched number to total number between e v ery tw o sampled frames to a v oid f alse detection caused by too fe w k e ypoints generated. In order to zoom into the location of boundaries, peaks are detected and filtered to tak e only the deep enough peaks to be re g arded as boundaries. 3.1. F eatur e Extraction Scale In v ariant Feature T ransform (SIFT) is an approach for detecting and e xtracti ng local feature de- scriptors that are reasonably in v ariant to changes in illumination, image noise, rotation, scaling, and small changes in vie wpoint. There are four major steps: Detection of scale-space e xtreme, accurate k e ypoint locali zation, orien- tation assignment, descriptor representation. ī€ scale-space peak selection: The first stage of computation searches o v er all scales and image locations. It is implemented ef ficiently by using a dif ference-of-Ga ussian function (DoG) to identify k e ypoint candidates for SIFT features that ar e in v ariant to scale and orientation. DoG scale space can be obtained from equation (1). D ( x; y ;  ) = ( G ( x; y ; k  )  G ( x; y ;  )) ī€ƒ I ( x; y ) (1) where * is the con v olution operation, I(x,y) is the gray v alue of pix el at (x,y) and G(x,y ,  ) is a v ariable-scale Gaussian k ernel defined as: G ( x; y ;  ) = 1 2   2 e  ( x 2 + y 2 ) = 2  2 (2) V ideo Shot Boundary Detection Using The Scale In variant F eatur e T r ansform and RGB ... (Z. El khattabi) Evaluation Warning : The document was created with Spire.PDF for Python.
2568 ISSN: 2088-8708 ī€ K eypoint localization: At each candidate location, a detailed model is fit to determine location and scale. K e ypoints are selected based on measures of their stability . Lo w contrast k e ypoints introduced by noise and edge response will be remo v ed. ī€ Orientation assignment: An orientation is assigned to each k e ypoint to achie v e in v ariance to image ro- tation. A neigbourhood is tak en around the k e ypoint location depending on the scale, and the gradient magnitude and direction is calculated in that re gion. An orientation histogram with 36 bins co v ering 360 de grees is created. ī€ k eypoint descriptor: A 16x16 neighborhood around the k e ypoint is tak en. It is di vided into 16 sub-blocks of 4x4 sizes. F or each sub-block, 8 bin orientation histogram is created. So a tot al of 128 bin v alues are a v ailable. It leads to a SIFT feature v ector of 128 dimensions. Color pro vides more discriminatory information than simple intensities. Although, RGB Color space is simple and v ery common. Hence, in our w ork, SIFT descriptors are computed for e v ery RGB channel indepen- dently , and the information a v ailable in the three dif ferent color spaces are combined, unlik e SIFT model that is designed only for grayscale information and misses important visual information re g arding color . 3.2. Shot boundary detection SIFT k e ypoints are e xtracted from frames of video and then ratios of matched k e ypoints number to total number between frame i and frame i+N are used to detect shot boundaries. The adv antage of feature matching is that it is in v ariant to af fine transformations; thus, we can e v en match objects after the y ha v e mo v ed. Figure 1 sho ws local feature matching between tw o frames. (a) Frames within the same shot. (b) Frames from dif ferent shots. Figure 1. Feature k e ypoints matching between tw o frames. The simi larity matching between tw o frames in the same shot is usually high, due to the similar image feature, objects and colors. Ho we v er , frames from dif ferent shots ha v e visual discontinui ty . As a result, the y ha v e no similarity matching or a lo w number of it. 3.2.1. The top do wn sear ch scheme T o a v oid unnecessary processing of video frames within an y shot, a search is first carried out by perform- ing similarit y matching for e v ery N th frame i n the video. It is a good solution for decreasing computational cost. Let us denote the i th frame of a video as F(i) . Then, the algorithm is conducted as follo ws (Figure 2): Figure 2. The top do wn search process. IJECE V ol. 7, No. 5, October 2017: 2565 – 2573 Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE ISSN: 2088-8708 2569 Each color channel obtained for each N th frame of the video i s subjected to feature e xtraction process (SIFT -RGB), the output of which is fed to similarity matching process among the successi v e frames that results in three similarity v alues for each i frame: ratioR, ratioG and ratioB. This similarity information is fused to obtain one ratio representing the matched similarities between F(i) and F(i+N) . The choice of using the ratios of matched features e xtracted to total number features, instead of comparing the number of feature k e ypoints with a prefix ed threshold, is referred to the f alse detection caused by the small number of k e ypoints in the frames with fe w objects and colors, whi ch generates a fe wer matched similarities e v en though the y are similar . The ratio for each color channel of the frame F i is defined as: r atioR ( i ) = 2 M r K r ( F i ) + K r ( F i + N )) (3) r atioG ( i ) = 2 M g K g ( F i ) + K g ( F i + N )) (4) r atioB ( i ) = 2 M b K b ( F i ) + K b ( F i + N )) (5) Where M r , M g and M b are the number of matches found respecti v ely for red, green and blue color planes between F i and F i + N . K r , K g and K b are the total number of feature k e ypoints e xtracted from each color plane of the frame .The final ratio obtained from the three ratios is defined as: R atio R GB ( i ) = r atioR + r atioG + r atioB 3 (6) The determination of the temporal sampling period N depends on the type of video content and the duration of the shots, if a sequence of successi v e frames is captured by man y cameras lik e in case of action mo vies, we can ha v e uncontinuous action and v ery short shots. Consequently , an entire shot may start and end up between the sampled frames and be missed. F or that, the choice of N must tak e into consideration the nature of video content. The temporal sampling period N is chosen to be N=25 (1 sec) in the e xample illustrated in figure 3. Figure 3. the o v ervie w of a video with N=25. In order to zoom into locations of shot boundaries , e xtrema peaks are detected to filter the v ery deep peaks to be tak en as boundaries. The peak detection function is used in [24] to find boundaries by comparing each minima peak with the pre vious and successi v e e xtrema peaks, using a threshold T=0.5 to compare the depth of the peak with the others. The boundaries detection function is described in Algorithm 1. P i is a peak and P t and P r are the left and right end of the peak. Dashed lines in figure 3 present the peaks detected with this function. V ideo Shot Boundary Detection Using The Scale In variant F eatur e T r ansform and RGB ... (Z. El khattabi) Evaluation Warning : The document was created with Spire.PDF for Python.
2570 ISSN: 2088-8708 Algorithm 1:Boundaries detection 1: F or i=1,2,3,... do 2: if ( P i < P i  1 and P i < P i +1 ) 3: then t=i-1; r=i+1; 4: while ( P t < P t  1 ) t=t-1; 5: while ( P r < P r +1 ) r=r+1; 6: if ( P i < P t *T or P i < P r *T) 7: then zoom in to [ F ( i  1) ī€ƒ N , F i ī€ƒ N ] 3.2.2. Determination of transition type T o determine if a shot is a hard cut or gradual transition, the mo ving a v erage v al ue of frames in the boundaries is calculated. The mo ving a v erage of frame t is defined as: Av er ag eR atio ( t ) = 1 N t  1 X i = t  N R atio R GB ( t ) (7) Where R atio R GB ( t ) is the ratio of matching feature k e ypoints obtained in equation (6) by fusing the three ratios r atioR , r atioG and r atioB of a frame t , this frame is detected as a boundary using the algorithm 1. The period N is used as a number of pre vious frames used with t he current frame t when calculating the mo ving a v erage. W e can distinguish transitions by measuring the dif ference of A ver a g eRatio(t) and R atio R GB ( t ) as described in algorithm 2. Algorithm 2: T ype of transition 1: F or t = t 1 ; t 2 ; :::; t n do ( t i is a shot boundary) 2: if ( Av er ag eR atio ( t )  R atio R GB ( t ) > =  ) 3: then 4: type of transition=cut boundary 5: else 6: type of transition=gradual transition A threshold  is used to detect transition types. In our e xperiments, the choice of an appropriate threshold  , has a high impact on the accurac y of the results. 4. EXPERIMENTS AND RESUL TS In order to e v aluate the performance of the proposed method and re v eal its adv antages o v er the other methods in literature, W e ha v e designed an e xperimental video dataset containing four types of videos (sport, ne ws, cartoon, mo vie) .The video sequences used are MPEG-4 compressed videos, with v arious dimensions and containing se v eral types of transitions, The Experiment dataset used for e v aluation are listed in table 1. T able 1. Information of e xperimental videos T ype Number of frames Size Duration Number of shots Sport 83525 640x360 3341 sec 411 Ne ws 45100 640x360 1804 sec 223 Cartoon 31855 1280x720 1385 sec 204 Mo vie 72749 1280x720 3163 sec 530 The performance results of the proposed method are sho wn as precision and recall v alues in T able 2. Precision and recall are defined as: P r ecision = N c N c + N f (8) IJECE V ol. 7, No. 5, October 2017: 2565 – 2573 Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE ISSN: 2088-8708 2571 R ecal l = N c N m + N c (9) Where N c , N f and N m are the numbers of correct, f alse and miss shot boundary detections, respecti v ely . T able 2. Ev aluation of the proposed method Abrupt Changes Gradual T ransition Precision Recall Precision Recall Sport 0.92 0.85 0.93 0.77 Ne ws 0.95 0.94 0.89 0.86 Cartoon 0.88 0.91 0.75 0.81 Mo vie 0.94 0.87 0.79 0.88 Figure 4 sho ws some shot boundaries detected f rom the e xperimental dataset. The transitions presented in figure 4 belong to a cut transition where there is a complete dissimilarity between tw o successi v e frames, and the ratio of matched k e ypoints is v ery small or null. (a) Example 1 of cut transition (frames 99 and 100). (b) Example 2 of cut transition (frames 230 and 231). Figure 4. Examples of tw o cut transitions detected in cartoon video. W e tested our method on some videos from the Open V ideo Project [25]. Figure 5 sho ws t he frames in the first gradual transition detected by our method on a video pro vided by The Open V ideo repository: (N ASA 25th Anni v ersary Sho w , se gment 1), we can see clearly that the changes and dissimilarities occur gradually between the successi v e frames. These v ariations are translated by the v alue of RGB ratio of matched similarities that decrease gradually between the frame 128 and 142. Figure 5. Example of gradual transition detected. The lo w recall rate in sports video is may be due to the short shots that are missed between the sampled frames. In contrast, the precision rates in this kind of videos are more than 90%. It sho ws that the method is ef fecti v e in detecting abrupt and gradual transitions.On the other side, in general, recall rates are lo w . This re v eals that some frames belonging to dif ferent shots were re g arded as similar . As a result, se v eral shot boundaries are missed. In ne ws vi d e o the precision rate and the recall rate are high (more than 90 %),because of the long shots and the e xistence of man y cut transition which are distinguished by the great changes between the frames. Accordingly , shot boundaries are well detected. Also, the choice of the temporal sampling period N as 1 second indicates that all the shots less than this v alue will be missed. The adaptation of the parameter N in accordance with the video sequences can increase the performance results by the reduction of miss or f alse shot boundary detection. The comparison of this method with the e xperimental results reported in other w orks based on SIFT , sho ws that the inte gration of the three col or channels R, G and B of video frames gi v es more precision in detecting shot boundaries than using only the grayscale channel. V ideo Shot Boundary Detection Using The Scale In variant F eatur e T r ansform and RGB ... (Z. El khattabi) Evaluation Warning : The document was created with Spire.PDF for Python.
2572 ISSN: 2088-8708 5. CONCLUSION In this w ork, a ne w algorithm is presented based on scale in v ariant feature transform adapted to the RGB color space. First, a top do wn search process is performed by comparing the ratio of matched k e ypoints e xtracted via SIFT for e v ery R, G and B channels of tw o video frames separated by a temporal sampling period N. Then, an algorithm is used to detect the shot boundaries. Finally , the mo ving a v erage of frames in the boundaries is calculated to determine the type of the transition by using a threshold. Our method is applied to dif ferent types of video and sho ws satisf actory performance in detecting abrupt changes and gradual transitions, b ut it can be impro v ed by using weighting coef ficients to calculate the ratioRGB from the three ratios(R,G and B), depending on the type of the video. In the future w orks,we aim to include performance impro v ements and minimizing the computational cost without decreasing the accurac y . REFERENCES [1] J. T . T . Mei, L.-X. T ang and X.-S. Hua, ā€œNear -lossless semantic video summarization and its applications to video analysis, ā€ A CM T r ansactions on Multimedia Computi ng , Communications, and Applications (T OMM) , v ol. 9, no. 3, June 2013. [2] R. Thompson, Gr ammar of the Shot , F . Press, Ed., 1998. [3] J. S. Boreczk y and L. A. Ro we, ā€œComparison of video shot boundary detection techniques, ā€ J ournal of Electr onic Ima ging , v ol. 5, no. 2, pp. 122–128, April 1996. [4] R. G. T apu, ā€œSe gmentation and structuring of video documents for inde xing applications, ā€ December 2012. [5] S. H. G. Jaf fre, Ph. Joly , ā€œThe s amo v a shot boundary detection for trecvid e v aluation 2004, ā€ in Pr oceedings of the TRECVID 2004 W orkshop, Gaither sb ur g , MD, USA, NIST , 2004. [6] B. Shahraray , ā€œScene change detection and content-based sampling of video sequences, ā€ in Pr oc. SPIE Digital V ideo Compr ession: Algorithms and T ec hnolo gies , v ol. 2419, 1995, pp. 2–13. [7] C.-L. Huang and B.-Y . Liao, ā€œ A rob ust scene-change detection method for video se gmentation, ā€ IEEE T r ans- actions on Cir cuits and Systems for V ideo T ec hnolo gy , v ol. 11, no. 12, pp. 1281–1288, December 2001. [8] D. S. Guru and M. Suhil, ā€œHistogram based split and mer ge frame w ork for shot boundary detecti o n, ā€ Min- ing Intellig ence and Knowledg e Explor ation, Lectur e Notes in Computer Science , v ol. 8284, pp. 180–191, December 2013. [9] D. Guru and M. Suhil, ā€œHistogram based split and mer ge frame w ork for shot boundary detection, ā€ Min- ing Intellig ence and Knowledg e Explor ation, Lectur e Notes in Computer Science , v ol. 8284, pp. 180–191, December 2013. [10] S. C. R. S. Jadon and K. K. Bisw as, ā€œ A fuzzy theoretic approach for video se gmentation using syntactic features, ā€ P attern Reco gnition Letter s , v ol. 22, no. 13, pp. 1359–1369, No v ember 2001. [11] L. Y . R. L. C. Y . . Z. R. Qu, Z., ā€œ A method of shot detection based on color and edge features, ā€ in 1st IEEE Symposium on W eb Society , SWS’09 , August 2009, pp. 1–4. [12] H. Z. P . Aigrain and D. P etk o v i c, ā€œContent-based representation and retrie v al of visual media: A state-of- the-art re vie w , ā€ Multimedia T ools and Applications , v ol. 3, no. 3, pp. 179–202, No v ember 1996. [13] S. M. P . P anchal and N. P atel, ā€œScene detection and retrie v al of video using motion v ector and occurrence rate of shot boundaries, ā€ in 2012 Nirma Univer sity International Confer ence on Engineering (NUiCONE) , December 2012, pp. 1–6. [14] X. H. Y . W . Muhammad Ab ul Hasan, Min Xu, ā€œ A camera motion histogram descriptor for video shot classi- fication, ā€ Multimedia T ools and Applications , v ol. 24, no. 74, p. 1107311098, December 2015. [15] F . B. F . Bayat, M. Shahram Moin, ā€œGoal detection in soccer video: Role-based e v ents detection approach, ā€ International J ournal of Electrical and Computer Engineering (IJECE) , v ol. 4, no. 6, pp. 979–988, 2014. [16] . N. H. S. Rashmi, B. S., ā€œV ideo shot boundary detection using midrange local binary pattern, ā€ in Interna- tional Confer ence on Advances in Computing , Communications and Informatics (ICA CCI),IEEE , September 2016, pp. 201–206. [17] A. M. E. M. M. Pournazari, F . Mahmoudi, ā€œV ideo summarization based on a fuzzy based incremental clus- tering, ā€ International J ournal of Electrical and Computer Engineering (IJECE) , v ol. 4, no. 4, pp. 593–602, 2014. [18] N. N. S. D. . G. E. Loukas, C., ā€œShot boundary detection in endoscopic sur gery videos using a v ariational bayesian frame w ork, ā€ International journal of computer assisted r adiolo gy and sur g ery , v ol. 11, no. 11, pp. 1937–1949, 2016. [19] K. T . S. K. M. . R. S. Thounaojam, D. M., ā€œ A genetic algorithm and fuzzy logic approach for video shot boundary detection, ā€ Computational intellig ence and neur oscience , no. 14, 2016. IJECE V ol. 7, No. 5, October 2017: 2565 – 2573 Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE ISSN: 2088-8708 2573 [20] E. A. A. K. N. P . . J. M. Hannane, R., ā€œ An ef ficient method for video shot boundary detection and k e yframe e xtraction using sift-point distrib ution histogram, ā€ International J ournal of M u l timedia Information Re- trie val , v ol. 2, no. 5, pp. 89–104, 2016. [21] W . X. Z. W . . H. P . Liu, G., ā€œShot boundary detection and k e yframe e xtraction based on scale in v ariant feature transform, ā€ in Eighth IEEE/A CIS International Confer ence on Computer and Information Science , ICIS 2009 , June 2009, pp. 1126–1130. [22] W . X. Z. A. . W . J. C hi, A., ā€œRe vie w of research on shot boundary detection algorithm of the compressed video domain in content-based video retrie v al technique, ā€ in DEStec h T r ansactions on Engineering and T ec hnolo gy Resear c h, (iceta) , 2016. [23] D. Lo we, ā€œDistincti v e image features from scale in v ariant k e ypoints, ā€ International J ournal of Computer V ision , v ol. 60, no. 2, pp. 91–110, 2004. [24] M. B irinci and S. Kiran yaz, ā€œ A perceptual scheme for fully automatic video shot boundary detection, ā€ Signal Pr ocessing: Ima g e Communication , v ol. 29, no. 3, pp. 410–423, March 2014. [25] The open video project. [Online]. A v ailable: https://open-video.or g/inde x.php BIOGRAPHIES OF A UTHORS Zaynab El khattabi is a Ph.D. student in F aculty of Sciences, Abdelmalek Essadi Uni v ersity , Mo- rocco . She is a Computer Sciences engineer , graduated in 2012 from National School of Applied Sciences, Abdelmalek Essadi Uni v ersity . She got a DEUG on Mathematics and computer Sciences in 2009 from F aculty of Sciences, Abdelmalek Essadi Uni v ersity . Her current research interests include image and video processing and focuses on video-content analysis and retrie v al. Y ouness T abii recei v ed his PhD in July 2010 from the National School of Computer Sciences and Systems Analysis, Mohammed V Uni v ersity-Rabat. He is a Professor at the National School of Applied Sciences of T etuan (ENSA T). He is a member in Ne w T echnology T rends T eam (NTT T eam) and the Head of Master: Embedded and Mobile S ystems. His research interest includes video processing and analysis , also interested by cloud security . He is the F ounder and Chair of International Conference on Big Data, Coul d and Applications (BDCA). He is a Guest-Editor of the International Journal of Cloud Computing in 2016. Abdelhamid Benkaddour got a MAS and a PhD in Applied Mathematics and Mechanics from Pierre et Marie Curie (P aris VI) Uni v ersity in June 1986 and 1990, respecti v ely , and a PhD in Mathematics from Abdelmalek Essaadi Uni v ersity in 1994. His research focuses on numerical analysis, scientific computing and computer science. V ideo Shot Boundary Detection Using The Scale In variant F eatur e T r ansform and RGB ... (Z. El khattabi) Evaluation Warning : The document was created with Spire.PDF for Python.