Content based video retrieval using deep learning feature extraction by modified VGG_16

The recent challenge faced by the users from the multimedia area is to collect the relevant object or unique image from the collection of huge data. During the classification of semantics, the media was allowed to access the text by merging the media with the text or content before the emergence of content based retrieval. After its presence, media retrieval process is made easier than earlier stages by adding the attributes to the media in the database using multi-dimensional feature vectors which are termed as descriptors. The identification this features has become major challenges, so to overcome this issue this paper focuses on a deep learning techniques named as Modified Visual Geometry Group _16, and the result of this techniques have been compared with the existing other feature extraction techniques such as conventional histogram of oriented gradients (HOG), local binary patterns (LBP) and convolution neural network (CNN) methods. In this scheme the video frame image retrieval is performed by assigning the indexing to the all video files in the database in order to perform the system more efficiently. Thus the system produces the top result matches for the similar query in comparison with the existing techniques based on accuracy, precision, recall and F1 score in optimized video frame retrieval.