A convolution Neural network takes up the role of a film editor that defines or classifies visual similarities between the given scene and a dataset of hundreds of different movies. A dataset of cinema frames was created from 100 movies, it consisted of extracted cinema frames of each movie which summed up to around 115,000 images. The reverse image search algorithm was trained on this dataset. The interface, when queried with a movie clip, outputS a series of images which were similar to the input. A selection of movie frames was done and few seconds featuring that parts were clipped out from the original movie. All the clipped-out sequences were aligned together in relation to the input and a parallel video was generated.