LAUSR

The popularity of video recordings either on mobile devices or video surveillance has contributed to the demand for video data applications. As a result, the management of video has become significant in the object retrieval process. However, the video object retrieval is considered as a major issue in the video objects management. This paper proposes an integrated optimization-based classifier for video object retrieval. Here, the input video is subjected to optical flow estimation using a spatio-temporal feature descriptor, named Histograms of Optical Flow Orientation and Magnitude (HOFM) for extracting the events and establishing the HOFM indexed database using the detected objects. Hence, object tracking is a major step, which detects and tracks the video objects using a hybrid model named Nearest Search Algorithm-Support Vector Regression (NSA-SVR). For the video retrieval, the training of Naive Bayes (NB) classifier is performed using the proposed Lion-Salp Swarm Algorithm (LSSA) on the indexed database of the tracked objects. Then, the recognized events interrelated to the query are subjected to the Naive Bayes classifier to retrieve the required video. The performance of the proposed integrated LSSA-Naive Bayes Classifier is found to be better than the existing methods with maximal precision of 82.985 percent, recall of 87.451 percent, and F-measure of 87.847 percent.