CAVDM: Cellular Automata Based Video Cloud Mining Framework for Information Retrieval
This work addresses efficient video data management for information retrieval, but it appears incremental as it builds on existing Cloud Mining and Cellular Automata techniques without claiming major breakthroughs.
The paper tackled the challenge of retrieving information from video data by proposing a Cellular Automata based framework for video Cloud Mining, which includes shot detection, key frame analysis, and hierarchical clustering to group similar shots for event detection as per user demand.
Cloud Mining technique can be applied to various documents. Acquisition and storage of video data is an easy task but retrieval of information from video data is a challenging task. So video Cloud Mining plays an important role in efficient video data management for information retrieval. This paper proposes a Cellular Automata based framework for video Cloud Mining to extract the information from video data. This includes developing the technique for shot detection then key frame analysis is considered to compare the frames of each shot to each others to define the relationship between shots. Cellular automata based hierarchical clustering technique is adopted to make a group of similar shots to detect the particular event on some requirement as per user demand.