Thousands of movies along with TV shows, documentaries are being produced each year around the world with different genres and languages. Making a movie scene impactful as well as original is challenging task for the director. On the other hand, users demands to retrieve similar scenes from their queries is also challenging task as there is no proper maintenance of database of movie scene videos with proper semantic tags associated with it. So to fulfill the requirement of these two (but not the least) application areas there is a need of content based retrieval system for movie scenes. Content based video retrieval is a problem of retrieving most similar videos to a given query video by analyzing the visual contents of videos. Traditional video level features based on key frame level hand engineered features which does not exploit rich dynamics present in the video. In this paper we propose a Content based Movie Scene Retrieval (CB-MSR) framework using spatio-temporal features learned by deep learning. Specifically deep CNN along with LSTM is deploy to learn spatio-temporal representations of video. On the basis of these learned features similar movie scenes can be retrieve from the collection of movies. Hollywood2 dataset is used to test the proposed system. Two types of features: spatial and spatio-temporal features are used to evaluate the proposed framework.

In this paper, examination another creamer substance storing and scattering structure for customer made substance (UGC). Despite the obviousness of developing the combination plot that manhandles the potential gains of sharp associations, offloading and decentralized limit and transport, to date such an arrangement has not yet been proposed again due to two fundamental reasons. Regardless, taking into account the extraordinary correspondence and energy cost of decentralized substance storing and transport for flexible systems. Second, due to the area these two head limitations by abusing the opportunity of substance replication, considering starting experience time and range of customers' encounters, and utilizing relational collaboration organizations for guileful dispersing. The current structure game plan makes a dispersed decentralized limit system with canny substance replication, which decreases convenient data traffic and gives the customers full control at immaterial cost which can be used to give flexible individual to individual correspondence organizations. Regardless, the results show that it is unbelievable to dependably recognize most powerful customers inside a neighborhood advance transport as they are astoundingly environment subordinate. In this paper extension to online casual associations (OSNs) has experienced tremendous improvement lately. These OSNs offer appealing techniques for electronic social affiliations and information sharing, yet likewise raise different security and insurance issues. Moreover, it A3P separates what the procedure can mean for the suitability of a plan based structure that supports redesigned web access functionalities, like substance filtering and revelation, taking into account tendencies demonstrated by end customers.