3d convolutional neural networks for television advertisement detection

Abstract

A method is provided to classify whether video content is likely to be an advertisement or a non-advertisement. A curated database of video content items that includes a plurality of different video content items that were each previously identified as being an advertisement, and a plurality of different video content items that were each previously identified as not being an advertisement, are used to train a 2D CNN and a 3D CNN. The training of the 2D CNN includes learning characteristic visual and spatial features of advertisement images in the video content items compared to non-advertisement images in the video content items, the training resulting in weights being defined for the 2D CNN. The training of the 3D CNN includes learning a temporal structure and relationship over multiple image frames of the advertisements in the video content items compared to non-advertisement image frames in the video content items, the training resulting in weights being defined for the 3D CNN. The trained 2D CNN and 3D CNN are then used to determine the probability that newly identified video content should be classified as an advertisement.