Advanced Evaluation Method for Video Retrieval System

Abstract

In the past few years, the multimedia storage has increased and costs of storing multi-media data has become cheaper. Which is why, there are large numbers of videos that are available in the video repositories. With development of the multi-media data types and available bandwidths. The proposed method transfers each video of database into scenes using color histogram based scene change detection algorithm and key frames are extracted. For key frames multiple features are obtained using straight forward rules. A new framework depending on CNNs (convolutional neural networks) is recommended to perform the content based video retrieval with the less storage cost also with higher search capability. The recommended framework subsists of extraction algorithm with respect to key-frame and the feature collection strategies. Particularly, the extraction algorithm of key-frame takes benefit of clustering idea; so in that case excessive information is taken out from video data and also the storage cost is highly shortened.  This work present a method uses the extracted features with convolutional neural network (CNN) for classification tasks. In this research paper, different types of videos will be used and the important features will be extracted using SIFT, after that we use the deep learning(CNN) process will be performed. The experimental results showed the efficiency and effectiveness of suggested approach.