The aim of the work is to identify the duplication in a video database with the aid of feature extraction techniques. The process includes extraction of image features (shape, color, and texture) for duplicate identification. The color contains 256 features, shape contains 200 features, the texture contains two different features namely gray-level co-occurrence matrix (GLCM) (22 features in 4 degrees) and grey-level run length matrix (GLRLM) (11 features) are extracted. In this paper, the preliminary work is to convert video into frames and then each frame into blocks subsequently including feature extraction. A query video is then considered for the same process of feature extraction and compared with the normal video. The distance between query video and normal video if found to be similar then the video identified as duplicate video. The results are performed for various evaluation matrix and plotted graphs are shown. The sensitivity value for whole feature extractions is 0.88, the specificity value for whole feature extractions is 0.83 and the accuracy value for whole extractions is 0.86. The entire process implemented in the working platform of MATLAB.
Near-duplicate video (NDV) detection is an important issue of copyright protection. However, the traditional detection methods are very imprecise and complex. To solve the problem, this paper introduces the opposition-based solution generation strategy into the conventional whale optimization algorithm (OWOA), creating a novel NDV detection method called the OWOA. The author detailed how to use the hybrid method to extract different types of features, ranging from color, shape to texture, and compared the OWOA with traditional feature extraction methods through experiments. The comparison shows that the OWOA achieved the optimal performance in DNV detection. The research findings can greatly assist regulatory authorities in monitoring and detecting edited contents.
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