Matthew Cooper, D.Sc.

Principal Research Scientist

Matthew Cooper

Matt Cooper is a principal research scientist at FXPAL.  He previously led the Integrative Analytics research theme.  He develops automatic analysis technologies for interactive applications including the exploration, retrieval, and reuse of multimedia information.  He also supports machine learning efforts across multiple projects.

At FXPAL, he has worked on a variety of projects including LoComixMeet, TalkMinerInteractive Video SearchProjectorBox, and the FXPAL Photo Application.

He earned his B.S., M.S., and D.Sc. in Electrical Engineering at Washington University in St. Louis, and is a senior member of the IEEE and a distinguished member of the ACM.

Co-Authors

Publications

2018
Publication Details
  • ACM Intl. Conf. on Multimedia Retrieval (ICMR)
  • Jun 11, 2018

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Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. Increasingly, professionals consume this content to augment or update specific skills rather than complete degree or certification programs. To better address the needs of this emergent user population, we describe a visual recommender system called MOOCex. The system recommends lecture videos {\em across} multiple courses and content platforms to provide a choice of perspectives on topics. The recommendation engine considers both video content and sequential inter-topic relationships mined from course syllabi. Furthermore, it allows for interactive visual exploration of the semantic space of recommendations within a learner's current context.
Publication Details
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Apr 21, 2018

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Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. This paper presents MOOCex, a technique that can offer a more flexible learning experience for MOOCs. MOOCex can recommend lecture videos across different courses with multiple perspectives, and considers both the video content and also sequential inter-topic relationships mined from course syllabi. MOOCex is also equipped with interactive visualization allowing learners to explore the semantic space of recommendations within their current learning context. The results of comparisons to traditional methods, including content-based recommendation and ranked list representation, indicate the effectiveness of MOOCex. Further, feedback from MOOC learners and instructors suggests that MOOCex enhances both MOOC-based learning and teaching.
Publication Details
  • Multimedia Modeling 2018
  • Feb 5, 2018

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This paper examines content-based recommendation in domains exhibiting sequential topical structure. An example is educational video, including Massive Open Online Courses (MOOCs) in which knowledge builds within and across courses. Conventional content-based or collaborative filtering recommendation methods do not exploit courses' sequential nature. We describe a system for video recommendation that combines topic-based video representation with sequential pattern mining of inter-topic relationships. Unsupervised topic modeling provides a scalable and domain-independent representation. We mine inter-topic relationships from manually constructed syllabi that instructors provide to guide students through their courses. This approach also allows the inclusion of multi-video sequences among the recommendation results. Integrating the resulting sequential information with content-level similarity provides relevant as well as diversified recommendations. Quantitative evaluation indicates that the proposed system, \textit{SeqSense}, recommends fewer redundant videos than baseline methods, and instead emphasizes results consistent with mined topic transitions.
Publication Details
  • arXiv
  • Jan 24, 2018

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Tutorials are one of the most fundamental means of conveying knowledge. Ideally when the task involves physical or digital objects, tutorials not only describe each step with text or via audio narration but show it as well using photos or animation. In most cases, online tutorial authors capture media from handheld mobile devices to compose these documents, but increasingly they use wearable devices as well. In this work, we explore the full life-cycle of online tutorial creation and viewing using head-mounted capture and displays. We developed a media-capture tool for Google Glass that requires minimal attention to the capture device and instead allows the author to focus on creating the tutorial's content rather than its capture. The capture tool is coupled with web-based authoring tools for creating annotatable videos and multimedia documents. In a study comparing standalone (camera on tripod) versus wearable capture (Google Glass) as well as two types of multimedia representation for authoring tutorials (video-based or document-based), we show that tutorial authors have a preference for wearable capture devices, especially when recording activities involving larger objects in non-desktop environments. Authors preferred document-based multimedia tutorials because they are more straightforward to compose and the step-based structure translates more directly to explaining a procedure. In addition, we explored using head-mounted displays (Google Glass) for accessing tutorials in comparison to lightweight computing devices such as tablets. Our study included tutorials recorded with the same capture methods as in our access study. We found that although authors preferred head-mounted capture, tutorial consumers preferred video recorded by a camera on tripod that provides a more stable image of the workspace. Head-mounted displays are good for glanceable information, however video demands more attention and our participants made more errors using Glass than when using a tablet, which was easier to ignore. Our findings point out several design implications for online tutorial authoring and access methods.
2017

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For tourists, interactions with digital public displays often depend on specific technologies that users may not be familiar with (QR codes, NFC, Bluetooth); may not have access to because of networking issues (SMS), may lack a required app (QR codes), or device technology (NFC); may not want to use because of time constraints (WiFi, Bluetooth); or may not want to use because they are worried about sharing their data with a third-party service (text, WiFi). In this demonstration, we introduce ItineraryScanner, a system that allows users to seamlessly share content with a public travel kiosk system.
Publication Details
  • ACM Document Engineering 2017
  • Aug 30, 2017

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In this paper, we describe DocHandles, a novel system that allows users to link to specific document parts in their chat applications. As users type a message, they can invoke the tool by referring to a specific part of a document, e.g., “@fig1 needs revision”. By combining text parsing and document layout analysis, DocHandles can find and present all the figures “1” inside previously shared documents, allowing users to explicitly link to the relevant “document handle”. Documents become first-class citizens inside the conversation stream where users can seamlessly integrate documents in their text-centric messaging application.
Publication Details
  • TRECVID Workshop
  • Mar 1, 2017

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This is a summary of our participation in the TRECVID 2016 video hyperlinking task (LNK). We submitted four runs in total. A baseline system combined on established vectorspace text indexing and cosine similarity. Our other runs explored the use of distributed word representations in combination with fine-grained inter-segment text similarity measures.
2016
Publication Details
  • ACM MM
  • Oct 15, 2016

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The proliferation of workplace multimedia collaboration applications has meant on one hand more opportunities for group work but on the other more data locked away in proprietary interfaces. We are developing new tools to capture and access multimedia content from any source. In this demo, we focus primarily on new methods that allow users to rapidly reconstitute, enhance, and share document-based information.
Publication Details
  • Document Engineering DocEng 2016
  • Sep 13, 2016

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In this paper we describe DocuGram, a novel tool to capture and share documents from any application. As users scroll through pages of their document inside the native application (Word, Google Docs, web browser), the system captures and analyses in real-time the video frames and reconstitutes the original document pages into an easy to view HTML-based representation. In addition to regenerating the document pages, a DocuGram also includes the interactions users had over them, e.g. mouse motions and voice comments. A DocuGram acts as a modern copy machine, allowing users to copy and share any document from any application.
Publication Details
  • ICME 2016
  • Jul 11, 2016

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Captions are a central component in image posts that communicate the background story behind photos. Captions can enhance the engagement with audiences and are therefore critical to campaigns or advertisement. Previous studies in image captioning either rely solely on image content or summarize multiple web documents related to image's location; both neglect users' activities. We propose business-aware latent topics as a new contextual cue for image captioning that represent user activities. The idea is to learn the typical activities of people who posted images from business venues with similar categories (e.g., fast food restaurants) to provide appropriate context for similar topics (e.g., burger) in new posts. User activities are modeled via a latent topic representation. In turn, the image captioning model can generate sentences that better reflect user activities at business venues. In our experiments, the business-aware latent topics are effective for adapting to captions to images captured in various businesses than the existing baselines. Moreover, they complement other contextual cues (image, time) in a multi-modal framework.
Publication Details
  • ACM International Conference on Multimedia Retrieval (ICMR)
  • Jun 6, 2016

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We propose a method for extractive summarization of audiovisual recordings focusing on topic-level segments. We first build a content similarity graph between all segments of all documents in the collection, using word vectors from the transcripts, and then select the most central segments for the summaries. We evaluate the method quantitatively on the AMI Meeting Corpus using gold standard reference summaries and the Rouge metric, and qualitatively on lecture recordings using a novel two-tiered approach with human judges. The results show that our method compares favorably with others in terms of Rouge, and outperforms the baselines for human scores, thus also validating our evaluation protocol.
Publication Details
  • Personal and Ubiquitous Computing (Springer)
  • Feb 19, 2016

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In recent years, there has been an explosion of services that lever- age location to provide users novel and engaging experiences. However, many applications fail to realize their full potential because of limitations in current location technologies. Current frameworks work well outdoors but fare poorly indoors. In this paper we present LoCo, a new framework that can provide highly accurate room-level indoor location. LoCo does not require users to carry specialized location hardware—it uses radios that are present in most contemporary devices and, combined with a boosting classification technique, provides a significant runtime performance improvement. We provide experiments that show the combined radio technique can achieve accuracy that improves on current state-of-the-art Wi-Fi only techniques. LoCo is designed to be easily deployed within an environment and readily leveraged by application developers. We believe LoCo’s high accuracy and accessibility can drive a new wave of location-driven applications and services.
2015
Publication Details
  • MM Commons Workshop co-located with ACM Multimedia 2015.
  • Oct 30, 2015

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In this paper, we analyze the association between a social media user's photo content and their interests. Visual content of photos is analyzed using state-of-the-art deep learning based automatic concept recognition. An aggregate visual concept signature is thereby computed for each user. User tags manually applied to their photos are also used to construct a tf-idf based signature per user. We also obtain social groups that users join to represent their social interests. In an effort to compare the visual-based versus tag-based user profiles with social interests, we compare corresponding similarity matrices with a reference similarity matrix based on users' group memberships. A random baseline is also included that groups users by random sampling while preserving the actual group sizes. A difference metric is proposed and it is shown that the combination of visual and text features better approximates the group-based similarity matrix than either modality individually. We also validate the visual analysis against the reference inter-user similarity using the Spearman rank correlation coefficient. Finally we cluster users by their visual signatures and rank clusters using a cluster uniqueness criteria.
Publication Details
  • ACM MM
  • Oct 26, 2015

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Establishing common ground is one of the key problems for any form of communication. The problem is particularly pronounced in remote meetings, in which participants can easily lose track of the details of dialogue for any number of reasons. In this demo we present a web-based tool, MixMeet, that allows teleconferencing participants to search the contents of live meetings so they can rapidly retrieve previously shared content to get on the same page, correct a misunderstanding, or discuss a new idea.
Publication Details
  • DocEng 2015
  • Sep 8, 2015

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Web-based tools for remote collaboration are quickly becoming an established element of the modern workplace. During live meetings, people share web sites, edit presentation slides, and share code editors. It is common for participants to refer to previously spoken or shared content in the course of synchronous distributed collaboration. A simple approach is to index with Optical Character Recognition (OCR) the video frames, or key-frames, being shared and let user retrieve them with text queries. Here we show that a complementary approach is to look at the actions users take inside the live document streams. Based on observations of real meetings, we focus on two important signals: text editing and mouse cursor motion. We describe the detection of text and cursor motion, their implementation in our WebRTC-based system, and how users are better able to search live documents during a meeting based on these detected and indexed actions.

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Location-enabled applications now permeate the mobile computing landscape. As technologies like Bluetooth Low Energy (BLE) and Apple's iBeacon protocols begin to see widespread adoption, we will no doubt see a proliferation of indoor location enabled application experiences. While not essential to each of these applications, many will require that the location of the device be true and verifiable. In this paper, we present LocAssure, a new framework for trusted indoor location estimation. The system leverages existing technologies like BLE and iBeacons, making the solution practical and compatible with technologies that are already in use today. In this work, we describe our system, situate it within a broad location assurance taxonomy, describe the protocols that enable trusted localization in our system, and provide an analysis of early deployment and use characteristics. Through developer APIs, LocAssure can provide critical security support for a broad range of indoor location applications.
Publication Details
  • IEEE Pervasive Computing
  • Jul 1, 2015

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Tutorials are one of the most fundamental means of conveying knowledge. In this paper, we present a suite of applications that allow users to combine different types of media captured from handheld, standalone, or wearable devices to create multimedia tutorials. We conducted a study comparing standalone (camera on tripod) versus wearable capture (Google Glass). The results show that tutorial authors have a slight preference for wearable capture devices, especially when recording activities involving larger objects.
Publication Details
  • Presented in "Everyday Telepresence" workshop at CHI 2015
  • Apr 18, 2015

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As video-mediated communication reaches broad adoption, improving immersion and social interaction are important areas of focus in the design of tools for exploration and work-based communication. Here we present three threads of research focused on developing new ways of enabling exploration of a remote environment and interacting with the people and artifacts therein.
2014

Multi-modal Language Models for Lecture Video Retrieval

Publication Details
  • ACM Multimedia 2014
  • Nov 2, 2014

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We propose Multi-modal Language Models (MLMs), which adapt latent variable models for text document analysis to modeling co-occurrence relationships in multi-modal data. In this paper, we focus on the application of MLMs to indexing slide and spoken text associated with lecture videos, and subsequently employ a multi-modal probabilistic ranking function for lecture video retrieval. The MLM achieves highly competitive results against well established retrieval methods such as the Vector Space Model and Probabilistic Latent Semantic Analysis. Retrieval performance with MLMs is also shown to improve with the quality of the available extracted spoken text.
Publication Details
  • DocEng 2014
  • Sep 16, 2014

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Distributed teams must co-ordinate a variety of tasks. To do so they need to be able to create, share, and annotate documents as well as discuss plans and goals. Many workflow tools support document sharing, while other tools support videoconferencing, however there exists little support for connecting the two. In this work we describe a system that allows users to share and markup content during web meetings. This shared content can provide important conversational props within the context of a meeting; it can also help users review archived meetings. Users can also extract shared content from meetings directly into other workflow tools.
Publication Details
  • Ubicomp 2014
  • Sep 9, 2014

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In recent years, there has been an explosion of social and collaborative applications that leverage location to provide users novel and engaging experiences. Current location technologies work well outdoors but fare poorly indoors. In this paper we present LoCo, a new framework that can provide highly accurate room-level location using a supervised classification scheme. We provide experiments that show this technique is orders of magnitude more efficient than current state-of-the-art Wi- Fi localization techniques. Low classification overhead and computational footprint make classification practical and efficient even on mobile devices. Our framework has also been designed to be easily deployed and lever- aged by developers to help create a new wave of location- driven applications and services.

Supporting media bricoleurs

Publication Details
  • ACM interactions
  • Jul 1, 2014

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Online video is incredibly rich. A 15-minute home improvement YouTube tutorial might include 1500 words of narration, 100 or more significant keyframes showing a visual change from multiple perspectives, several animated objects, references to other examples, a tool list, comments from viewers and a host of other metadata. Furthermore, video accounts for 90% of worldwide Internet traffic. However, it is our observation that video is not widely seen as a full-fledged document; dismissed as a media that, at worst, gilds over substance and, at best, simply augments text-based communications. In this piece, we suggest that negative attitudes toward multimedia documents that include audio and video are largely unfounded and arise mostly because we lack the necessary tools to treat video content as first-order media or to support seamlessly mixing media.
Publication Details
  • Fuji Xerox Technical Report, No. 23, 2014, pp. 34-42
  • Feb 20, 2014

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Video content creators invest enormous effort creating work that is in turn typically viewed passively. However, learning tasks using video requires users not only to consume the content but also to engage, interact with, and repurpose it. Furthermore, to promote learning with video in domains where content creators are not necessarily videographers, it is important that capture tools facilitate creation of interactive content. In this paper, we describe some early experiments toward this goal. A literature review coupled with formative field studies led to a system design that can incorporate a broad set of video-creation and interaction styles.
2013
Publication Details
  • IUI 2013
  • Mar 19, 2013

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We describe direct video manipulation interactions applied to screen-based tutorials. In addition to using the video timeline, users of our system can quickly navigate into the video by mouse-wheel, double click over a rectangular region to zoom in and out, or drag a box over the video canvas to select text and scrub the video until the end of a text line even if not shown in the current frame. We describe the video processing techniques developed to implement these direct video manipulation techniques, and show how there are implemented to run in most modern web browsers using HTML5's CANVAS and Javascript.
Publication Details
  • SPIE Electronic Imaging 2013
  • Feb 3, 2013

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Video is becoming a prevalent medium for e-learning. Lecture videos contain useful information in both the visual and aural channels: the presentation slides and lecturer's speech respectively. To extract the visual information, we apply video content analysis to detect slides and optical character recognition (OCR) to obtain their text. Automatic speech recognition (ASR) is used similarly to extract spoken text from the recorded audio. These two text sources have distinct characteristics and relative strengths for video retrieval. We perform controlled experiments with manually created ground truth for both the slide and spoken text from more than 60 hours of lecture video. We compare the automatically extracted slide and spoken text in terms of accuracy relative to ground truth, overlap with one another, and utility for video retrieval. Experiments reveal that automatically recovered slide text and spoken text contain different content with varying error profiles. Additional experiments demonstrate higher precision video retrieval using automatically extracted slide text.