{"id":506,"date":"2015-06-18T09:21:07","date_gmt":"2015-06-18T13:21:07","guid":{"rendered":"https:\/\/sites.temple.edu\/tudsc\/?p=506"},"modified":"2023-01-24T12:50:16","modified_gmt":"2023-01-24T16:50:16","slug":"digital-video-analysis-and-retrieval-part-ii-introduction-of-digital-video-analysis-tools","status":"publish","type":"post","link":"https:\/\/sites.temple.edu\/tudsc\/2015\/06\/18\/digital-video-analysis-and-retrieval-part-ii-introduction-of-digital-video-analysis-tools\/","title":{"rendered":"Digital Video Analysis and Retrieval (PART II) -Introduction of Digital Video Analysis Tools"},"content":{"rendered":"

By Ping Feng<\/p>\n

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In Part I, I discussed the\u00a0Cinemetrics<\/a>\u00a0project, a prominent database with film editing statistics. Though it is still largely based on manual collaboration, its decent amount and richness of data have been utilized by many scholars to either conduct further cinematic analysis or theorize their methodologies. For example, Mike Baxter has worked on papers such as\u00a0Evolution in Hollywood editing patterns?<\/em><\/a> and\u00a0Cutting patterns in D.W.Griffith\u2019s silent feature films<\/em> <\/a>by using descriptive data and R code (Baxter, 2012). More of his theoretical principle and data analysis methodology can be found\u00a0here<\/a>\u00a0.<\/p>\n

Another example is Nick Rederfern’s work. He maintains a\u00a0blog – Research Into Film<\/a>\u00a0–\u00a0where some of his film studies are presented \u00a0by using quantitative approach theorized by Cinemetrics.<\/p>\n

Shot Logger<\/a> is another open source database assembled the Cinemetrics but at a\u00a0smaller scale. It provides visual style analysis and a database with editing statistics for 928 instances of 246 films and TV programs and a gallery of 282,157 frames captured (Jeremy Butler, data updated till April 8, 2015). The drawback for this site is that the data, such as Average Short Length, Median Short Length, is mainly presented numerically, not graphically.<\/p>\n

However, in addition to the sheer volume of the digital video available in competing users\u2019 attention, the unique nature of digital video with both temporal and spatial elements also requires a more automatic way to collect, retrieve, and analyze the data. For instance, in order to collect the number of cuts or the appearance of certain face in a one-hour long video for analysis\u00a0manually,\u00a0it will be both time-consuming and expensive to transit and stream the giant size of video for at least one hour or so. Therefore, the need for efficient analyzing and retrieving video images has called for an automatic way of metadata extraction and indexing.<\/p>\n

One\u00a0project, completed by\u00a0undergraduate Fredric Brodbeck, is also called “Cinemetrics”, but with a\u00a0different concept. He developed an precise\u00a0tool to “automatically” extract, process, and visualize movie data. A video demo of his project can\u00a0be found\u00a0here<\/a>, and his code is available here<\/a>.<\/p>\n

Some organizations have already developed tools or algorithms to automatically extract, analyze, and visualize the metadata of digital video editing statistics or visual images. For example,<\/p>\n