| { | |
| "paper_id": "W98-0205", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T06:05:17.976773Z" | |
| }, | |
| "title": "Visualization for Large Collections of Multimedia Information", | |
| "authors": [ | |
| { | |
| "first": "Dave", | |
| "middle": [], | |
| "last": "Himmel", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Mark", | |
| "middle": [], | |
| "last": "Greaves", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "Natural Language Processing Applied Research and Technology Boeing Shared Services Group", | |
| "institution": "", | |
| "location": { | |
| "postBox": "P.O. Box 3707", | |
| "postCode": "7L-43, 98124", | |
| "settlement": "Seattle", | |
| "region": "MS, WA" | |
| } | |
| }, | |
| "email": "mark.t.~reaves@boein_o.com" | |
| }, | |
| { | |
| "first": "Anne", | |
| "middle": [ | |
| "Kao Steve" | |
| ], | |
| "last": "Poteet", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "Natural Language Processing Applied Research and Technology Boeing Shared Services Group", | |
| "institution": "", | |
| "location": { | |
| "postBox": "P.O. Box 3707", | |
| "postCode": "7L-43, 98124", | |
| "settlement": "Seattle", | |
| "region": "MS, WA" | |
| } | |
| }, | |
| "email": "" | |
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| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "Organizations that make use of large amounts of multimedia material (especially images and video) require easy access to such information. Recent developments in computer hardware and algorithm design have made possible content indexing of digital video information and efficient display of 3D data representations. This paper describes collaborative work between Boeing Applied Research & Technology (AR&T), Carnegie Mellon University (CMU), and the Battelle Pacific Northwest National Laboratories (PNNL). to integrate media indexing with computer visualization to achieve effective content-based access to video information. Text metadata, representing video content, was extracted from the CMU Informedia system, processed by AR&T's text analysis software, and presented to users via PNNL's Starlight 3D visualization system. This approach shows how to make multimedia information accessible to a text-based visualization system, facilitating a global view of large collections of such data. We evaluated our approach by making several experimental queries against a library of eight hours of video segmented into several hundred \"video paragraphs.\" We conclude that search performance of Inforrnedia was enhanced in terms of ease of exploration by the integration with Starlight.", | |
| "pdf_parse": { | |
| "paper_id": "W98-0205", | |
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| "abstract": [ | |
| { | |
| "text": "Organizations that make use of large amounts of multimedia material (especially images and video) require easy access to such information. Recent developments in computer hardware and algorithm design have made possible content indexing of digital video information and efficient display of 3D data representations. This paper describes collaborative work between Boeing Applied Research & Technology (AR&T), Carnegie Mellon University (CMU), and the Battelle Pacific Northwest National Laboratories (PNNL). to integrate media indexing with computer visualization to achieve effective content-based access to video information. Text metadata, representing video content, was extracted from the CMU Informedia system, processed by AR&T's text analysis software, and presented to users via PNNL's Starlight 3D visualization system. This approach shows how to make multimedia information accessible to a text-based visualization system, facilitating a global view of large collections of such data. We evaluated our approach by making several experimental queries against a library of eight hours of video segmented into several hundred \"video paragraphs.\" We conclude that search performance of Inforrnedia was enhanced in terms of ease of exploration by the integration with Starlight.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "Boeing uses very large collections of data in the process of engineering and manufacturing commercial jet airplanes and in delivering complex military and space systems. The company also creates large amounts of information in the form of manuals and other documents to be delivered with these products. The need to control the cost of creating and accessing this information is motivation for new methods of indexing and searching computerized digital data.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In 1996, the Natural Language Processing Group at Boeing AR&T began a collaboration with PNNL to jointly develop the Starlight information visualization system. AR&T developed the Text Processing Toolset (TPT) to perform indexing and querying operations in high-dimensional document spaces, and PNNL developed software for 3D graphics presentations. Although Starlight has a rich visual presentation, it processes only text and static imagery, and lacks any inherent capability for extracting content from multimedia data. Concurrently. as part of ongoing research into digital libraries, the AR&T Multimedia Group began support of the CMU Informedia project and obtained a demonstration system for searching indexed digital video information. At the heart of Informedia is a subsystem that creates text metadata that is descriptive of digital video content. This suggested awav to integrate the two systems.", | |
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| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The Informedia Project has established a large on-line digital video library, incorporating video assets from WQED/Pittsburgh. The project is creating intelligent, automatic mechanisms for populating the library and allowing for its full-content and knowledgebased search and segment retrieval. Our approach applies several techniques for content-based searching and video-sequence retrieval. Content is conveyed in both the narrative (speech and language) and the image. Only by the collaborative interaction of image, speech, and natural-language understanding technology can we successfully populate, segment, index, and search diverse video collections with satisfactory recall and precision.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Informedia", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "The Informedia Project uses the Sphinx-II speech recognition system to transcribe narratives and dialogues automatically. The resulting transcript is then processed with methods of natural language understanding to extract subjective descriptions and mark potential segment boundaries where significant semantic changes occur. Comparative difference measures are used in processing the video to mark potential segment boundaries. Images with small lfistogram disparity are considered to be relatively equivalent. By detecting significant changes in the weighted histogram of each successive frame, a sequence of images can be grouped into a segment. This simple and robust method for segmentation is fast and can detect 90% of the scene changes in video.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Informedia", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Segment breaks produced by image processing are examined along with the boundaries identified by the natural language processing of the transcript, and an improved set of segment boundaries are heuristically derived to partition the video library into sets of segments, or \"video paragraphs.\" The reader can find more in-depth discussions of the Informedia project and technologies in References 1-4.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Informedia", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Starlight was originally developed as an interactive information visualization environment for the US Army Intelligence and Security Command (INSCOM). It is designed to integrate several types of data (unstructured and structured text documents, geographic information, and digital imagery') into a single analysis space for rapid comparison of content and interrelationships (see reference 5). In this section, we will concentrate on the Starlight text processing and indexing functions.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Starlight", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "A major problem with incorporating free-text documents into a visualization environment is that each document must be coded so that it can be clustered with other documents. The Boeing TPT is a prototype software engine that supports automatic coding and categorization of documents, concept-based querying, and visualization over large text document databases. The TPT combines techniques from statistics, linear algebra, and computational linguistics in order to take account of the total context in which words occur in a given document or qUery; it statistically compares a document context with similar contexts from other documents in the database. Through this technique, document sets can be represented in a way that supports visualization and analysis by the presentation components of Starlight.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Starlight", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "The TPT performs two functions: First. it provides a powerful and flexible mechanism for concept-based searching over large text databases; second, it automatically assigns individual text units to coordinates in a userconfigurable 3D semantic space. Both of these functions derive from the TPT core technique of representing large numbers of text units as points in a higher dimensional space and performing similarity calculations in this space. Conceptually, the flow of data through the TPT is diagrammed in Ouch, --I / New Ten'n I Space .J", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Starlight", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "Celcp~te Vector D~tence~", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Ranked Matoh Ust", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Prior to TPT indexing, a text collection must be preprocessed in three stages involving manual intervention: First, the text is divided into units with topical granularity that best correlates with the expected query patterns. The units can be titles, subject lines, abstracts, individual paragraphs or an entire document. It can also be a caption or a piece of transcribed text from a video. Next, the text is \"tokenized\" into individual words and phrases. Finally, a list of \"'stopwords,'\" or ignored terms, is chosen. These include determiners (e.g., a, the), conjunctions (e.g., and, or), and relatives (e.g., what, which), and certain domain-specific terms.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Figure 2 -TPT Processing Flow Diagram", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "After preprocessing, operation of the TPT indexing system is automatic (see Figure 2) . The software builds a document/term matrix, performs several transformation and dimension reduction calculations, and stores output matrices in an object-oriented database for use by the Starlight visualization component.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 76, | |
| "end": 85, | |
| "text": "Figure 2)", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Figure 2 -TPT Processing Flow Diagram", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Users of Starlight can visually explore the topical structure of a large text database by navigating through a 3D topic space where each item is represented as a point in a scatterplot. Items with close visual proximity have similar content. The TPT provides a selection of dimensions (with associated topic words), any three of which can potentially be selected for axes of the scatterplot. Display of video paragraphs occurs in the context of a web page containing a video viewer and the text transcript; Figure 5 shows an example. When the user selects a particular document within Starlight. a browser displays the HTML page. The browser was coded in Java and the MPEG viewer is a Microsoft ActiveMovie control. Each digital video file was kept intact, that is. the video paragraphs were not partitioned into separate files; rather, paragraphs are viewed by playing from the specified \"In\" frame to the \"Out\" frame of the appropriate MPEG file. advantages. Neither system was designed for narrow, precise data querying, rather Starlight features all-inclusive views and Informedia facilitates iterative, progressive narrowing of focus. Motivation for integrating the systems was twofold: Give Starlight access to multimedia data, and investigate possible advantages of the global overview that Starlight can bring to the Informedia database.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 507, | |
| "end": 515, | |
| "text": "Figure 5", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Figure 2 -TPT Processing Flow Diagram", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We collected the following list of video titles from diverse sources at Boeing: ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Figure 2 -TPT Processing Flow Diagram", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 PBS", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Figure 2 -TPT Processing Flow Diagram", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Although both Informedia and Starlight separately exhibit powerful capabilities for accessing large collections of data, it was interesting to speculate about synergistic", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Results", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The video data was indexed by Informedia, then we passed the resulting metadata to Starlight as described above. Starlight presents the entire collection of hundreds of video paragraphs as a scatterplot of points for viewing, which can be color-coded by video title. We immediately observed that items with like colors tended to cluster together, verifying that TPT processing was effective in bringing together topically related items.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Results", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In an experiment to evaluate the further effects of integration, we posed several queries to both Informedia and Starlight, noted commonalties and differences in response, then used the global view of Starlight to find other items with similar content and to discover additional search terms. After Starlight reported query \"hits,\" we observed the region in 3D space encompassed by the video file containing the most hits. Using the cursor brush to see abstracts, we examined ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Results", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The last three columns indicate a positive result by showing additional video paragraphs and search terms relating to the initial query. The additional items varied in degree of relevance, but the cost of the new information is low in that only a few seconds were required to examine each. This illustrates the capacity of the integrated approach as an interactive tool for ready access to video content.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Informedia Hits", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "This effort clearly succeeded in the goal of providing access to multimedia content for the Starlight system. We have also shown the usefullness of Starlight global visualization of text metadata for video content. all items (of any color) within this region, and noted interesting terms. Because the axes were topically labeled, additional terms were also available from the axis in the vicinity of the cluster. Table 1 shows the results of this experiment.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 413, | |
| "end": 420, | |
| "text": "Table 1", | |
| "ref_id": "TABREF1" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Conclusion", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Total Same as Informedia The concept of bringing text metadata into Starlight is extensible to image, sound, animation, and other media, which suggests further experimentation with other forms of multimedia information and methods of generating metadata.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Starlight Hits", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Both Informedia indexing and Starlight processing require some manual intervention. In order for these approaches to be efficient and cost-effective, we must develop fully automatic methods for creating and processing text metadata for multimedia information. It may be possible to do this by compromising the quality of metadata (perhaps by using unedited speech recognition from Informedia); a future experiment would be to attempt this compromise and discover the effect on search performance.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Starlight Hits", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "Our thanks go to Ricky Houghton and Bryan Maher at the Carnegie Mellon University Informedia project, and to John Risch, Scott Dowson, Brian Moon, and Bruce Rex at the Battelle Pacific Northwest National Laboratories Starlight project for their excellent work leading to this result. The Boeing team also includes Dean Billheimer, Andrew Booker, Fred Holt, Michelle Keim, Dan Pierce, and Jason Wu.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Acknowledgments", | |
| "sec_num": null | |
| } | |
| ], | |
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| "FIGREF0": { | |
| "text": "illustrates the video searching facilities of Informedia, which include: \u2022 Filmstrip (lower-left of Figure 1) -Select thumbnail images to view video paragraph. \u2022 Selective play (upper right of Figure 1) -Prev/next paragraph, prev/next term hit. \u2022 Cursor browse -Abstracts and search terms available in filmstrip and play window. \u2022 Text query (upper left of Figure 1) -Terms parsed from natural language query. \u2022 Skim (not shown) -View video in 10% of normal time.", | |
| "uris": null, | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF1": { | |
| "text": "Informedia Search Screen", | |
| "uris": null, | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF2": { | |
| "text": "on Sim ila rity ~]'-I \"--.-._..~,,~,,. F\"Ter.rr,\u00d7 D,:,o~ | Frequency' I Vector Ct~ ~te Matrix Metr~ Oecomloostio~ F ooo,mo q J LC\u00b0~\u00b0ne~ts._l \"-'-\"----.i. I\"\" Do~mer,~-1 I h New Term I F Weigl-,t q ._,...._,--,----' J\" I,_ Space ,..I ~", | |
| "uris": null, | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF3": { | |
| "text": "shows a Starlight visualization screen containing a 3D scatterplot of the 322-item video metadata extracted from InformecUa: each axis is labeled with the dominant topics measured by the TPT. At the right ofFigure 3is an example query with results.", | |
| "uris": null, | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF4": { | |
| "text": "Integration of the two systems required the insertion of two processing steps, each involving new software development.Figure 4 shows the flow of data in and between the two systems; new integration elements are shown as shaded boxes. The two key elements are: (1) Extract video paragraph text metadata from Informedia, and (2) Display selected video paragraphs. .................................................................", | |
| "uris": null, | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF5": { | |
| "text": "Integration of Informedia and Starlight \u2022 Video file name \u2022 \"In\" and \"Out\" frames in the video file (for viewing the video paragraph) \u2022 Title of video paragraph \u2022 Abstract of video paragraph \u2022 Transcript text Extraction of metadata is accomplished by a C program that reads ASCII text and control parameters from several flies in the Informedia system and writes a collection of items compatible with TPT processing. Each item represents one video paragraph and includes the following fields:", | |
| "uris": null, | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF6": { | |
| "text": "Figure 5 -Video Paragraph Display", | |
| "uris": null, | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "TABREF1": { | |
| "text": "Experimental Results", | |
| "num": null, | |
| "html": null, | |
| "type_str": "table", | |
| "content": "<table><tr><td>Initial Query</td><td/></tr><tr><td>How do I use the emergency</td><td>6</td></tr><tr><td>exits?</td><td/></tr><tr><td>What is fly by wire?</td><td>4</td></tr><tr><td>Tell me about scientific</td><td>12</td></tr><tr><td>visualization.</td><td/></tr><tr><td>What do you have on the Boeing</td><td/></tr><tr><td>merger with Rockwell?</td><td/></tr></table>" | |
| } | |
| } | |
| } | |
| } |