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VIAN: a Visual Annotation Tool for Film Analysis Eurographics Conference on Visualization (EuroVis) 2019 Volume 38 (2019), Number 3 M. Gleicher, H. Leitte, and I. Viola (Guest Editors) VIAN: A Visual Annotation Tool for Film Analysis Gaudenz Haltery, Rafael Ballester-Ripollz, Barbara Flueckigery, and Renato Pajarolaz Figure 1: Left: Single segment analysis in VIAN showing its timeline and perceptual color widgets. Right: Global view showing a vocabulary navigator for interactive semantic analysis and annotation, keyword-filtered ensemble of screenshots, and visualization of the film’s frames over time according to their chromatic qualities. Abstract While color plays a fundamental role in film design and production, existing solutions for film analysis in the digital human- ities 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 incorpo- rates 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. CCS Concepts •Human-centered computing ! Visualization systems and tools; •Applied computing ! Media arts; 1. Introduction theoretical framework for visualization (see [Hef16, Stu16, Ole17] for an extended investigation). Digital analysis and visualization techniques have become cen- tral in film and media studies. Since the emergence of computer- Unfortunately, existing color-oriented visualization systems are assisted film analysis, numerous design and exploratory systems usually based on features computed for a complete frame. This have been proposed that focus e.g. on network analysis, aver- coarse granularity results in a loss of semantic information that lim- age shot length [Tsi05], or framing. There is a growing inter- its its potential scientific value for various applications. In addition, est among film scholars in extracting and analyzing color fea- the visualizations are often not matched to color perception. Fi- tures in the context of film visualization based on digital meth- nally, even if they provide enough information for a film scholar, ods. A number of those visualization systems are specifically tai- they are usually not implemented flexibly enough to adapt to exist- lored for color analysis and aesthetics in films, including those by ing, well established workflows. Brodbeck (Cinemetrics [Bro11]) Ferguson [Fer16], Burghardt et ∗ ∗ al. [BKW16, BHE 17] or Olesen [OMG 16]. Such visualizations Such workflows typically include a manual segmentation stage produce significantly more meaningful results when backed by a that partitions a given film into temporal segments, then clas- sifies them based on one or more sets of keywords. This task is usually performed in a film annotation software. Obviously, y Department of Film Studies, University of Zurich, Switzerland these tools vary greatly in their target audience, platform/imple- z Department of Informatics, University of Zurich, Switzerland mentation and supported functionalities. Typical use cases include c 2019 The Author(s) Computer Graphics Forum c 2019 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. G. Halter, R. Ballester-Ripoll, B. Flueckiger, and R. Pajarola / VIAN: A Visual Annotation Tool for Film Analysis textual annotation, speech analysis, and transcription or gesture 2. Related Work analysis. While traditional tools were mainly desktop applica- In this section we review literature related to qualitative film anal- tions, browser-based solutions have recently become more pop- ysis and media annotation in the field of digital humanities, then ular [CLA, Lig, SAT]. While all these annotation tools were de- discuss existing visualizations for film material. veloped for film analysis, they do not feature color visualizations (see [Giu10, Giu14, MEHK∗17, Flu17] for an overview). 2.1. Qualitative Data Analysis and Media Annotation in DH After the segmentation step, the second stage in the typical film analysis research flow, classification, is usually based on vocabular- Qualitative data analysis is a fundamental framework for digital ies. Annotation tools may follow different approaches for this task. humanities (DH) scholars, and the landscape of software packages Many tools hardcode and attach classification labels to all annota- for this task is commensurately large. We refer the reader to Mel- ∗ tion strings, which is arguably an error-prone task. More elaborate gar et al. [MEHK 17] for a broad survey that includes an exten- tools implement the concept of a so called controlled vocabulary, sive comparison of over 50 existing tools related to film analysis which restricts the terms that are allowed for each string, for exam- and their purpose within DH, grouped by functionality type. Three ple using a collection of checkboxes. While this is definitely suf- of those important functionalities are partially covered in VIAN: ficient in many applications, it does not scale well in practice. For professional video annotation, qualitative analysis, and automatic instance, some segments may not find any matching term within video analysis. Schoeffmann et al. [SHH15] investigated common the controlled vocabulary, or the GUI design may be overwhelmed and newly arising ways to interact with multimedia content. We by the sheer number of terms contained in large vocabularies. largely follow the Web Annotation Data Model [YSC17]: each an- notation consists of a target and an annotation content (the body). Contributions Since annotations often only refer to parts of a media resource (for example, time intervals or spatial regions), selectors are used to de- We present VIAN (VIsual ANnotator), a novel film annotation soft- fine the annotated region. Notably, the nature of selectors depends ware that places emphasis on visual aspects of film style and its on the type of resource that is annotated. color aesthetics. We take a step towards the qualitative data analy- sis (QDA) paradigm, where classification is an integral part of de- sign. Semantics between classification and their target are more ex- 2.2. Film Visualization plicit in VIAN than in any prior annotation tools. Several visualization methods for color distribution in films have VIAN has been developed in collaboration with an extensive film been proposed, see [Stu16] and [Ole17] for a more derailed dis- studies project that has qualitatively analyzed a corpus of approx- cussion. Some fundamental methods have been developed for the imately 400 films through all periods of film history. As part of visualization of artworks and other media [Man12, Man15, RG17, this project’s manual labeling stage, a total of over 17,000 tempo- KL17], Frederic Brodbeck arranged color schemes in circles to ral segments were classified using approximately 1,200 keywords express palette changes over time, Kevin Ferguson used the so- organized in vocabularies that produced more than half a million called z-projections to compute a mean frame of the complete film summations. Our goal was to develop a comprehensive tool that by calculating the mean along the temporal axis for each pixel can be used both to set up new projects and to enrich datasets that location and normalizing the final frame. Such approaches use are available. By incorporating semantic segmentation, we improve elements from temporal data visualization [AMST11] and have been created using existing tools like ImageJ [SRE12] or Image- the issue of semantic preservation of film features in visualizations ∗ and allow users to establish a link between their verbal classifica- Plot [Man13, Hef16, OMG 16]. tion and the visual content of a frame. As an evaluation, we con- A well known type of visualization to summarize film material ducted interviews with three current users of VIAN (see Sec. 6.2), are MovieBarcodes [BKW18], which are produced by either com- who assessed the system usability for a range of tasks of interest. puting the average color for each frame or reducing their dimension Specifically, VIAN’s main contributions include: by one, and plot the result as a color barcode [BKW16,BHE∗17] or columns of sorted pixels over time [Cut16]. While these movie bar- • A system featuring integrated annotation and film exploration codes are of good use for distant reading tasks, they expressiveness that allows for both temporal and spatial selection, several de- depends heavily on the film material’s color composition and due grees of time granularity (film/segment/screenshot), colorimetric to the averaging process often overemphasize brown tints. Casey analysis, 2D/3D navigation among several possible color spaces, and Williams [CW14] divided the input frames in a 4 × 4 grid image plots, and an array of related user interface widgets. and computed color histograms for each cell to compare tempo- • To the best of our knowledge, VIAN is the first such system that ral segments in films and visualized their Euclidian distance using supports deep learning-driven segmentation for spatially-aware a similarity matrix. Hohman et al. [HSSS17] developed an interac- color analysis. VIAN goes beyond basic foreground-background tive timeline visualization to explore the connection between color segmentation and refines it further into key semantic and
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