Employing Deep Learning Framework to Support Location Management for the Audio-Visual Industry

This paper presents a specialized tool for film producers and managers to extract useful information from images of different locations. The resulting framework can be used for image retrieval and other applications related to film production and location scouting. The framework uses deep learning models to extract labels, colors, seasons, emotions, and building architecture from input images. The methodology is described in detail, outlining the different steps involved in image processing and feature extraction. The label extraction module uses a transfer learning approach with pre-trained CNN models fine-tuned on a custom dataset of 106 classes. The color extraction module uses the LAB color space, a device-independent and perceptually uniform color space, in image processing applications, to accurately identify the dominant color, while the season extraction module extracts seasonal information from input images. The emotion extraction module identifies the emotion portrayed in an image, and the architectural style module automatically identifies the style of buildings in photos. Finally, the embedding module combines various vectors representing different image aspects to create a general embedding for each image.