VIAN: A Visual Annotation Tool for Film Analysis

Abstract

While color plays a fundamental role in film design and production, existing solutions for film analysis in the digital humanities address perceptual and spatial color information only tangentially. We introduce VIAN, a visual film annotation system centered on the semantic aspects of film color analysis. The tool enables expert‐assessed labeling, curation, visualization and Classification of color features based on their perceived context and aesthetic quality. It is the first of its kind that incorporates foreground‐background information made possible by modern deep learning segmentation methods. The proposed tool seamlessly integrates a multimedia data management system, so that films can undergo a full color‐oriented analysis pipeline.

A survey of visual analytics techniques for machine learning

“…To this end, Moehrmann et al [32] used an SOM-based visualization to place similar images together, allowing users to label multiple similar images of the same class in one go. This strategy is also used by Khayat et al [28] to identify social spambot groups with similar anomalous behavior, Kurzhals et al [29] to label mobile eye-tracking data, and Halter et al [24] to annotate and analyze primary color strategies used in films. Apart from placing similar items together, other strategies, like filtering, have also been applied to find items of interest for labeling.…”

KALMUS: tools for color analysis of films

“…Some, such as the Distance Viewing Toolkit (Arnold & Tilton, 2020), are very general and handle several aspects of a moving image at once (sound, color, camera angle, etc.). Others, such as VIAN (Halter et al, 2019), allow for a "closer," more interactive distant viewing.…”

A Survey of Visual Analytics Techniques for Machine Learning

“…Before Model Building Improving Data Quality (31) [3], [11], [14], [16], [17], [18], [25], [45], [61], [91], [96], [101], [102], [118], [123], [125], [136], [144], [157], [193], [202], [204], [205], [214], [228], [229], [232], [257], [259], [268], [275] Improving Feature Quality (6) [109], [132], [184], [195], [223], [239] During Model Building Model Understanding (30) [28], [38], [56], [71], [79], [84], [104], [115], [116], [119], [120], [137],…”

“…To this end, Moehrmann et al [193] used an SOM-based visualization to place similar images together, allowing users to label multiple similar images of the same class in one go. This strategy is also used by Khayat et al [125] to identify social spambot groups with similar anomalous behavior, Kurzhals et al [136] to label mobile eyetracking data, and Halter et al [96] to annotate and analyze primary color strategies used in films. Apart from placing similar items together, other strategies, like filtering, have also been applied to find items of interest for labeling.…”