Identifying Objects in Multimedia Databases with Computer Vision
Blog post from Roboflow
The article explores the use of computer vision technology to identify and classify characters in comic strips, specifically focusing on the Peanuts comic strip, as a way to demonstrate advancements in artificial intelligence and multimedia object recognition. It discusses the historical context of using computers to analyze comedy, highlighting previous research on automatic comedy generation and comic book analysis. The text details a project at Uninettuno University that experimented with different approaches to character recognition, including Haar Cascade Classification and a custom-built Convolutional Neural Network (CNN), ultimately finding the YOLOv5 model trained on Roboflow to be the most effective. The article emphasizes the challenges in detecting characters in comics due to their semi-structured nature and variability, and it showcases how the YOLOv5 model, aided by Roboflow's tools, provided accurate predictions and precise bounding boxes. The successful application of this model is suggested as a stepping stone for broader multimedia classification tasks, indicating the potential for enhanced searchability of media through similar techniques.