A Study on Video Segmentation using Fuzzy C-Means Techniques M. Mary Shanthi Rani1, P. Chitra2 and G. Shanthi3 Assistant Professor1, Ph.D Scholar2 and M.Phil Scholar3 Department of Computer Science and Applications The Gandhigram Rural Institute-Deemed University, Gandhigram, Tamilnadu, India
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[email protected] Abstract Video segmentation is a process of partitioning a video sequence into frames for meaningful segments. This technique is divided as object tracking; motion capturing and clustering Video Segmentation is the major issue that involved in retrieving and storing the video data. The fuzzy theoretic scheme provides a mechanism for characterizing scene transitions through soft decisions. Recently Fuzzy logic has been widely used for effective video segmentation. In this paper, describes about an overview of existing fuzzy c-means clustering based techniques used in video segmentation and their performance are analyzed and discussed.
class or cluster is similar as possible, while the items are belonging to a different classes as dissimilar as possible. Clusters are identified by similarity measures such as distance, connectivity, and intensity. Different similarity measures are based on the data or application. In this paper, Section 1 describes about an overview of Fuzzy cmean algorithm. Section 2 investigates the comparative study of Fuzzy C-means based video segmentation techniques. Section 3 examines the experimental analysis and result presented and analyzed. In the last section , we conclude this paper with possible future directions.
Keywords: Video segmentation, Clustering, Object tracking, Fuzzy c-means.
1. Introduction In recent days, social media is playing a vital role of our day to day life. It connects people through the internet and increase the progressive education system and business tricks. In current situation, various social media has been used such as giant, facebook, whatsapp, twitter which involves transmitting more video than image or text. Video segmentation is an emerging advancement in current world applications. It has a variety of applications Figure 1: Block diagram of Video Segmentation such as Sports, News, Military, Traffic control system, Video surveillance, Medical, Smart grid, Multimedia and Video segmentation method is shown in Fig 1 Navy etc. The facebook is now working with a virtual Graphical representation of It contains seven blocks to reality video. perform the operations and get the Input video, preprocessing, calculate histogram difference, Calculate Video Segmentation is an important process for motion optimal clusters, frame change detection, frame picture and video research. The complexity of segmentation and segemented Video Output using MPEG-4 segmentation is based on some issues such as occlusion, format files. cluttering, and low contrast edges, object overlapping etc. Usually, these problems will affect the progression of 2. Overview of Fuzzy C- means Algorithm image segmentation. A segmentation method could be Fuzzy C-means (FCM) is a method used for object recognition, occlusion boundary estimation within motion or stereo system. Recently, researchers of clustering which allows one piece of data to belong to have incorporated Fuzzy C-means (FCM) algorithm for two or more clusters. Fuzzy C Means (FCM) is one of the most commonly used fuzzy clustering techniques for improving the performance of video segmentation.[1] different degree estimation problems . It provides a Fuzzy clustering is a form of clustering in which method that solves how to group data points that populate each data point contains more than one cluster. some multidimensional space into a specific number of Clustering involves for assigning data points to clusters or different clusters which must be known a priori. FCM homogeneous classes, such that the data items are in same Computational Methods, Communication Techniques and Informatics ISBN: 978-81-933316-1-3
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employs fuzzy partitioning such that a data point can belong to several groups with the degree of membership matrix U is constructed of elements that have value between 0 and 1. The aim of FCM is to find cluster centres that minimize a dissimilarity function.[2] . After each iteration membership and cluster centers are updated according to the formula:
(3) Step 3: Compute the fuzzy centers 'vj' using:
(4)
Step 4: Repeat step 2 and step 3 until the minimum 'J' value is achieved . where, 'n' is the number of data points. (5)
'vj' represents the jth cluster center. 'm' is the fuzziness index m € [1, ∞].
Where, „k‟ is the iteration step.„β‟ is the termination criterion between [0, 1]. „U = (µij)n*c‟ is the fuzzy membership matrix. „J‟ is the objective function.
'c' represents the number of cluster center. 'µij' represents the to jth cluster center.
membership
of ith data Start
'dij' represents
the
Euclidean
distance
between i data and j cluster center. th
th
Give iterative thershold and intialize fuzzy partition matrix randomly U(o)
Main objective of fuzzy c-means algorithm is to minimize: Calculate the cluster center matrix V
Where , '||xi – vj||' is the Euclidean distance between ith data th and j cluster center.
Compute Euclidean Distance
2.1 Fuzzy C-means Clustering Algorithm This algorithm works by assigning membership to each data point corresponding to each cluster center on the basis of distance between the cluster center and the data point. More the data is near to the cluster center more is its membership towards the particular cluster center. Clearly, summation of membership of each data point should be equal to one. Let X = {x1, x2, x3 ..., xn} be the set of data points and V = {v1, v2, v3 ..., vc} be the set of centers.
Update fuzzy partition matrix U (S + 1)
S=S+1
||U(S+1) – U(S) || ≤
No
=
Yes Output cluster result
Step1: Randomly select „c‟ cluster centers.
Figure 2: FCM Flow Diagram Step2: Calculate the fuzzy membership 'µij' using:
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\
Advantages FCM algorithm yields better quality then k-means algorithm. where, k-means data points must exclusively belong to one cluster center. Here, data point is assigned membership to each cluster center as a result of which data point may belong to more than one cluster centre . Disadvantages FCM algorithm assigns apriori specification of the number of clusters, with lower value of β we get the better result but at the expense of more number of iteration. Euclidean distance is used to measure the unequally weighted and underlying factors.
3. Comparative Study on Video Segmentation using Fuzzy C-means Algorithm In this section, a critical review of the various approaches available for video segmentation using fuzzy c- means clustering algorithm is presented. Chi-chun lo et al. (2001) have proposed HBFCM clustering algorithm which is composed of three phases: the feature extraction phase, the clustering phase, and the key-frame selection phase. In the first phase, differences between color histogram are extracted as features. In the second phase, the fuzzy c-means FCM is used to group features into three clusters: the shot change SC cluster, the suspected shot change SSC cluster, and the no shot change NSC cluster. In the last phase, shot change frames are identified from the SC. and the SSC, and then used to segment video sequences into shots. Finally, key frames are selected from each shot.[1] K.Mahesh et al. (2012) This paper describes about the proposed technique that uses the fuzzy C- means approach for object extraction. The proposed approach uses the joint use of frame difference algorithm with the background subtraction method as well as consecutive frame difference for segmentation of static and dynamic objects. The proposed technique is evaluated by varying video sequences and the efficiency is analyzed by calculating the statistical measures and kappa coefficient. [2]
means of subtracting the current frame from the previous frame, to identify background.[3] V.Kalist et al. (2015) In this paper, prior to segmentation, the satellite images are transformed from RGB color space into HSL space. HSL color space approximate the human vision and represents the colors in more perceptual way than the RGB representation. The segmentation of satellite images in RGB and HSL color space is compared and the experimental result shows the competence of the proposed approach.[4] Ebrahim Asadi et al. (2015) The author proposed a novel algorithm of new keyframe extraction system that produces static video summaries, using fuzzy c-means clustering. We choose a frame with maximum membership grade for any cluster as keyframe. Number of clusters is estimated with a simple method and the summaries produced by users are used for evaluation.[5] 4. Experimental Results and Discussion This section investigates the existing methods for video segmentation using Fuzzy C-means algorithm. The performance are evaluated by metrices such as Error rate(1) and Accuracy rate (2). Table 1 shows the performance comparison result of five algorithms according to the specified mean error rate and mean accuracy rate. The Accuracy rate and Error are defined as follows: Accuracy rate = nmAS/n
(4)
Error rate = n:AS/nw
(5) Where, nmAS is the number of matching keyframes from automatic summary(AS), n:AS is the number of non matching keyframes from AS and nUS is the number of keyframes from user summary(US) [11].
Lakshmi S et al. (2013) This paper presents a robust background removal algorithms using Fuzzy C-Means Clustering and is a novel method for background removal that processes only some pixels of every image. The technique of Region of interest (ROI) is applied for objects in the image or frame which is located with the help of edge detector. Once the region is detected, only that area will be segmented instead of processing the whole image. The author detects the foreground object with the help of edge detector and combines the Fuzzy cmeans clustering algorithm to segment the object by
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Table 1: Comparative Analysis of Fuzzy C-means Clustering
It is observed from Table 1 that video summarization using Fuzzy C-means cluster gives high mean accuracy rate (0.75) with low mean error rate (0.30) and also the hybrid fuzzy c-means clustering algorithm yields high accuracy rate (0.84). Despite accuracy rates, the mean error rates of other methods are equally high as shown in Table 1.
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[7] [8]
5. Conclusion Segmentation is an important process for advance image analysis using computer vision. In this paper, we reviewed Fuzzy C-means based video segmentation algorithm which are widely used in recent image and video processing application.
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