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An Effectual Video Indexing and Retrieval Model: A Comparative Study

Video indexing and retrieval are considered to be the most crucial factor that has to be concentrated in multimedia search engine based investigations. The analysis with multimedia content may enhance the accuracy of the indexing and retrieval system, which is performed with two different phases. Former is content-based, and the latter is context-based annotation. The previous annotation includes both higher level and lower level features, which is acquired from pixel intensity; similarly, the latter is based on semantic video details. Here, an extensive analysis is done with three different methods known as Fuzzy based SVM classifier, logit boost ensemble classifier, and Naive Bayes classifier, where feature extraction is concentrated on various factors. These diverse methods pretend to carry of retrieval of the video effectually with specific advantages and disadvantages. Here, numerical analysis is done with different factors like accuracy, sensitivity, specificity, F-measure, Recall, and so on. The total amount of frames considered for computation is also determined. The videos are attained from the available online dataset, and initial pre-processing is carried out to make the quality of image frames to be superior. The overall analysis shows the performance of classifiers with improved prediction accuracy for video retrieval.