Abstract
This paper proposed and developed hybrid approach for extraction of key-frames from video sequences from stationary camera. This method first uses histogram difference to extract the candidate key frames from the video sequences, later using Background subtraction algorithm (Mixture of Gaussian) was used to fine tune the final key frames from the video sequences. This developed approach show considerable improvement over the state-of-the art techniques and same is reported in this paper.
Highlights
Key frame is a frame in a video that provide the best summary of the video content
Key frame extraction is essential for many video processing applications like video summary, video analysis, video organization and video compression
The following tables shows the comparison between a widely-applied algorithm for key frame extraction namely Sequence Difference Histogram Algorithm (SDIF) and the proposed algorithm i.e., Improved Hierarchical Clustering Algorithm
Summary
Key frame is a frame in a video that provide the best summary of the video content. Many videos are emerging on the internet, due to complexity of these videos it becomes more critical to search and catch required video quickly and effectively. A video stream will contain frames, shots, scenes and sequences. The extracted key frames must summarize the characteristics of the video, all the key frames in a time sequence gives visual summary of the video. In the process of video indexing and video retrieval it includes the analysis of structure for detecting shot boundaries, extracting key frames and segment scenes; feature extraction for object features and motion features; video annotation for building a semantic video index; query is returned for searching the desired video in video database using the index and similarity features; and video browsing and feedback for response to a query returned to browse in the form of video summary and subsequent search results are optimized [1,2]
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