A Novel Cluster Based Video Object Segmentation for Key Frame Extraction
Keywords:
Gaussian Kernel FCM, Clustering, Key Frame, Segmentation, oppositional-based BCOAbstract
Video key frames are the abstraction of content rich frames of a shot or a video that best reflects the nature of the whole video without redundancy. Object based key frame extraction techniques are capable of extracting key frames that are semantic. These techniques needs to extract the required objects or region through video object segmentation. The segmentation of objects is achieved by Fuzzy C-Means clustering as it distinguishes well across object boundaries. In this paper, Oppositional based Border Collie Optimization algorithm is proposed along with Gaussian Kernel FCM to optimize the centroids of clusters. The accuracy of the segmented objects are evaluated in terms of SSIM, BDE and VoI with the SBM-RGBD dataset. The resultant frames with segmented objects are compared with consecutive frames for change of pose of objects using key points features. When there is a considerable variations between two frames, one of the frames is selected as a key frame. The experimental results showed that the proposed BCOKFE technique improves the accuracy of the extracted key frames to 92% for the WEB data set.
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