Matthew Cooper, D.Sc.

Senior Research Scientist

Matthew Cooper

Matt Cooper is a research scientist at FXPAL, and currently leads the social and enterprise media group. He develops automatic content analysis technologies for interactive applications including the exploration, retrieval, and reuse of multimedia information.

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 both the ACM and the IEEE.

Co-Authors

Publications

2011
Publication Details
  • ACM Multimedia 2011
  • Nov 28, 2011

Abstract

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This paper describes methods for clustering photos that include both time stamps and location coordinates. We present versions of a two part method that first detects clusters using time and location information independently. These candidate clusters partition the set of time-ordered photos. A subset of the candidate clusters is selected by an efficient dynamic programming procedure to optimize a cost function. We propose several cost functions to design clusterings that are coherent in space, time, or both. One set of cost functions minimizes inter-photo distances directly. A second set maximizes an information measure to select clusterings for consistency in both time and space across scale.
Publication Details
  • IS&T and SPIE International Conference on Multimedia Content Access: Algorithms and Systems
  • Jan 23, 2011

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This paper describes research activities at FX Palo Alto Laboratory (FXPAL) in the area of multimedia browsing, search, and retrieval. We first consider interfaces for organization and management of personal photo collections. We then survey our work on interactive video search and retrieval. Throughout we discuss the evolution of both the research challenges in these areas and our proposed solutions.
2010

Reverted Indexing for Feedback and Expansion

Publication Details
  • ACM Conference on Information and Knowledge Management (CIKM 2010)
  • Oct 26, 2010

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Traditional interactive information retrieval systems function by creating inverted lists, or term indexes. For every term in the vocabulary, a list is created that contains the documents in which that term occurs and its relative frequency within each document. Retrieval algorithms then use these term frequencies alongside other collection statistics to identify the matching documents for a query. In this paper, we turn the process around: instead of indexing documents, we index query result sets. First, queries are run through a chosen retrieval system. For each query, the resulting document IDs are treated as terms and the score or rank of the document is used as the frequency statistic. An index of documents retrieved by basis queries is created. We call this index a reverted index. Finally, with reverted indexes, standard retrieval algorithms can retrieve the matching queries (as results) for a set of documents (used as queries). These recovered queries can then be used to identify additional documents, or to aid the user in query formulation, selection, and feedback.

TalkMiner: A Search Engine for Online Lecture Video

Publication Details
  • ACM Multimedia 2010 - Industrial Exhibits
  • Oct 25, 2010

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TalkMiner is a search engine for lecture webcasts. Lecture videos are processed to recover a set of distinct slide images and OCR is used to generate a list of indexable terms from the slides. On our prototype system, users can search and browse lists of lectures, slides in a specific lecture, and play the lecture video. Over 10,000 lecture videos have been indexed from a variety of sources. A public website now allows users to experiment with the search engine.

TalkMiner: A Lecture Webcast Search Engine

Publication Details
  • ACM Multimedia 2010
  • Oct 25, 2010

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The design and implementation of a search engine for lecture webcasts is described. A searchable text index is created allowing users to locate material within lecture videos found on a variety of websites such as YouTube and Berkeley webcasts. The index is created from words on the presentation slides appearing in the video along with any associated metadata such as the title and abstract when available. The video is analyzed to identify a set of distinct slide images, to which OCR and lexical processes are applied which in turn generate a list of indexable terms. Several problems were discovered when trying to identify distinct slides in the video stream. For example, picture-in-picture compositing of a speaker and a presentation slide, switching cameras, and slide builds confuse basic frame-differencing algorithms for extracting keyframe slide images. Algorithms are described that improve slide identification. A prototype system was built to test the algorithms and the utility of the search engine. Users can browse lists of lectures, slides in a specific lecture, or play the lecture video. Over 10,000 lecture videos have been indexed from a variety of sources. A public website will be published in mid 2010 that allows users to experiment with the search engine.
2009
Publication Details
  • ACM Multimedia 2009 Workshop on Large-Scale Multimedia Retrieval and Mining
  • Oct 23, 2009

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We describe an efficient and scalable system for automatic image categorization. Our approach seeks to marry scalable “model-free” neighborhood-based annotation with accurate boosting-based per-tag modeling. For accelerated neighborhood-based classification, we use a set of spatial data structures as weak classifiers for an arbitrary number of categories. We employ standard edge and color features and an approximation scheme that scales to large training sets. The weak classifier outputs are combined in a tag-dependent fashion via boosting to improve accuracy. The method performs competitively with standard SVM-based per-tag classification with substantially reduced computational requirements. We present multi-label image annotation experiments using data sets of more than two million photos.
Publication Details
  • Proceedings of TRECVID 2008 Workshop
  • Mar 1, 2009

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In 2008 FXPAL submitted results for two tasks: rushes summarization and interactive search. The rushes summarization task has been described at the ACM Multimedia workshop [1]. Interested readers are referred to that publication for details. We describe our interactive search experiments in this notebook paper.
Publication Details
  • IUI '09
  • Feb 8, 2009

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We designed an interactive visual workspace, MediaGLOW, that supports users in organizing personal and shared photo collections. The system interactively places photos with a spring layout algorithm using similarity measures based on visual, temporal, and geographic features. These similarity measures are also used for the retrieval of additional photos. Unlike traditional spring-based algorithms, our approach provides users with several means to adapt the layout to their tasks. Users can group photos in stacks that in turn attract neighborhoods of similar photos. Neighborhoods partition the workspace by severing connections outside the neighborhood. By placing photos into the same stack, users can express a desired organization that the system can use to learn a neighborhood-specific combination of distances.
2008
Publication Details
  • Fuji Xerox Technical Report
  • Dec 15, 2008

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We have developed an interactive video search system that allows the searcher to rapidly assess query results and easily pivot off those results to form new queries. The system is intended to maximize the use of the discriminative power of the human searcher. The typical video search scenario we consider has a single searcher with the ability to search with text and content-based queries. In this paper, we evaluate a new collaborative modification of our search system. Using our system, two or more users with a common information need search together, simultaneously. The collaborative system provides tools, user interfaces and, most importantly, algorithmically-mediated retrieval to focus, enhance and augment the team's search and communication activities. In our evaluations, algorithmic mediation improved the collaborative performance of both retrieval (allowing a team of searchers to find relevant information more efficiently and effectively), and exploration (allowing the searchers to find relevant information that cannot be found while working individually). We present analysis and conclusions from comparative evaluations of the search system.
Publication Details
  • ACM Multimedia 2008 Workshop: TrecVid Summarization 2008 (TVS'08)
  • Oct 26, 2008

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In this paper we describe methods for video summarization in the context of the TRECVID 2008 BBC Rushes Summarization task. Color, motion, and audio features are used to segment, filter, and cluster the video. We experiment with varying the segment similarity measure to improve the joint clustering of segments with and without camera motion. Compared to our previous effort for TRECVID 2007 we have reduced the complexity of the summarization process as well as the visual complexity of the summaries themselves. We find our objective (inclusion) performance to be competitive with systems exhibiting similar subjective performance.
Publication Details
  • ACM Conf. on Image and Video Retrieval (CIVR) 2008
  • Jul 7, 2008

Abstract

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We have developed an interactive video search system that allows the searcher to rapidly assess query results and easily pivot on those results to form new queries. The system is intended to maximize the use of the discriminative power of the human searcher. This is accomplished by providing a hierarchical segmentation, streamlined interface, and redundant visual cues throughout. The typical search scenario includes a single searcher with the ability to search with text and content-based queries. In this paper, we evaluate new variations on our basic search system. In particular we test the system using only visual content-based search capabilities, and using paired searchers in a realtime collaboration. We present analysis and conclusions from these experiments.
Publication Details
  • TRECVid 2007
  • Mar 1, 2008

Abstract

Close
In 2007 FXPAL submitted results for two tasks: rushes summarization and interactive search. The rushes summarization task has been described at the ACM Multimedia workshop. Interested readers are referred to that publication for details. We describe our interactive search experiments in this notebook paper.
2007
Publication Details
  • TRECVID Video Summarization Workshop at ACM Multimedia 2007
  • Sep 28, 2007

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This paper describes a system for selecting excerpts from unedited video and presenting the excerpts in a short sum- mary video for eciently understanding the video contents. Color and motion features are used to divide the video into segments where the color distribution and camera motion are similar. Segments with and without camera motion are clustered separately to identify redundant video. Audio fea- tures are used to identify clapboard appearances for exclu- sion. Representative segments from each cluster are selected for presentation. To increase the original material contained within the summary and reduce the time required to view the summary, selected segments are played back at a higher rate based on the amount of detected camera motion in the segment. Pitch-preserving audio processing is used to bet- ter capture the sense of the original audio. Metadata about each segment is overlayed on the summary to help the viewer understand the context of the summary segments in the orig- inal video.
Publication Details
  • IEEE Intl. Conf. on Semantic Computing
  • Sep 17, 2007

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We present methods for semantic annotation of multimedia data. The goal is to detect semantic attributes (also referred to as concepts) in clips of video via analysis of a single keyframe or set of frames. The proposed methods integrate high performance discriminative single concept detectors in a random field model for collective multiple concept detection. Furthermore, we describe a generic framework for semantic media classification capable of capturing arbitrary complex dependencies between the semantic concepts. Finally, we present initial experimental results comparing the proposed approach to existing methods.
Publication Details
  • ACM Conf. on Image and Video Retrieval 2007
  • Jul 29, 2007

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This paper describes FXPAL's interactive video search application, "MediaMagic". FXPAL has participated in the TRECVID interactive search task since 2004. In our search application we employ a rich set of redundant visual cues to help the searcher quickly sift through the video collection. A central element of the interface and underlying search engine is a segmentation of the video into stories, which allows the user to quickly navigate and evaluate the relevance of moderately-sized, semantically-related chunks.
Publication Details
  • IEEE Transactions on Multimedia
  • Apr 1, 2007

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We present a general approach to temporal media segmentation using supervised classification. Given standard low-level features representing each time sample, we build intermediate features via pairwise similarity. The intermediate features comprehensively characterize local temporal structure, and are input to an efficient supervised classifier to identify shot boundaries. We integrate discriminative feature selection based on mutual information to enhance performance and reduce processing requirements. Experimental results using large-scale test sets provided by the TRECVID evaluations for abrupt and gradual shot boundary detection are presented, demonstrating excellent performance.
2006
Publication Details
  • Interactive Video; Algorithms and Technologies Hammoud, Riad (Ed.) 2006, XVI, 250 p., 109 illus., Hardcover.
  • Jun 7, 2006

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This chapter describes tools for browsing and searching through video to enable users to quickly locate video passages of interest. Digital video databases containing large numbers of video programs ranging from several minutes to several hours in length are becoming increasingly common. In many cases, it is not sufficient to search for relevant videos, but rather to identify relevant clips, typically less than one minute in length, within the videos. We offer two approaches for finding information in videos. The first approach provides an automatically generated interactive multi-level summary in the form of a hypervideo. When viewing a sequence of short video clips, the user can obtain more detail on the clip being watched. For situations where browsing is impractical, we present a video search system with a flexible user interface that incorporates dynamic visualizations of the underlying multimedia objects. The system employs automatic story segmentation, and displays the results of text and image-based queries in ranked sets of story summaries. Both approaches help users to quickly drill down to potentially relevant video clips and to determine the relevance by visually inspecting the material.

Visualization in Audio-Based Music Information Retrieval

Publication Details
  • Computer Music Journal Vol. 30, Issue 2, pp. 42-62, 2006.
  • Jun 6, 2006

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Music Information Retrieval (MIR) is an emerging research area that explores how music stored digitally can be effectively organized, searched, retrieved and browsed. The explosive growth of online music distribution, portable music players and lowering costs of recording indicate that in the near future most of recorded music in human history will be available digitally. MIR is steadily growing as a research area as can be evidenced by the international conference on music information retrieval (ISMIR) series soon in its sixth year and the increasing number of MIR-related publications in the Computer Music Journal as well as other journals and conferences.
2005

Seamless presentation capture, indexing, and management

Publication Details
  • Internet Multimedia Management Systems VI (SPIE Optics East 2005)
  • Oct 26, 2005

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Technology abounds for capturing presentations. However, no simple solution exists that is completely automatic. ProjectorBox is a "zero user interaction" appliance that automatically captures, indexes, and manages presentation multimedia. It operates continuously to record the RGB information sent from presentation devices, such as a presenter's laptop, to display devices, such as a projector. It seamlessly captures high-resolution slide images, text and audio. It requires no operator, specialized software, or changes to current presentation practice. Automatic media analysis is used to detect presentation content and segment presentations. The analysis substantially enhances the web-based user interface for browsing, searching, and exporting captured presentations. ProjectorBox has been in use for over a year in our corporate conference room, and has been deployed in two universities. Our goal is to develop automatic capture services that address both corporate and educational needs.
Publication Details
  • World Conference on E-Learning in Corporate, Government, Healthcare, & Higher Education (E-Learn 2005)
  • Oct 24, 2005

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Automatic lecture capture can help students, instructors, and educational institutions. Students can focus less on note-taking and more on what the instructor is saying. Instructors can provide access to lecture archives to help students study for exams and make-up missed classes. And online lecture recordings can be used to support distance learning. For these and other reasons, there has been great interest in automatically capturing classroom presentations. However, there is no simple solution that is completely automatic. ProjectorBox is our attempt to create a "zero user interaction" appliance that automatically captures, indexes, and manages presentation multimedia. It operates continuously to record the RGB information sent from presentation devices, such as an instructor's laptop, to display devices such as a projector. It seamlessly captures high-resolution slide images, text, and audio. A web-based user interface allows students to browse, search, replay, and export captured presentations.
Publication Details
  • INTERACT 2005, LNCS 3585, pp. 781-794
  • Sep 12, 2005

Abstract

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A video database can contain a large number of videos ranging from several minutes to several hours in length. Typically, it is not sufficient to search just for relevant videos, because the task still remains to find the relevant clip, typically less than one minute of length, within the video. This makes it important to direct the users attention to the most promising material and to indicate what material they already investigated. Based on this premise, we created a video search system with a powerful and flexible user interface that incorporates dynamic visualizations of the underlying multimedia objects. The system employes an automatic story segmentation, combines text and visual search, and displays search results in ranked sets of story keyframe collages. By adapting the keyframe collages based on query relevance and indicating which portions of the video have already been explored, we enable users to quickly find relevant sections. We tested our system as part of the NIST TRECVID interactive search evaluation, and found that our user interface enabled users to find more relevant results within the allotted time than other systems employing more sophisticated analysis techniques but less helpful user interfaces.
Publication Details
  • ACM Transactions on Multimedia Computing, Communications, and Applications
  • Aug 8, 2005

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Organizing digital photograph collections according to events such as holiday gatherings or vacations is a common practice among photographers. To support photographers in this task, we present similarity-based methods to cluster digital photos by time and image content. The approach is general, unsupervised, and makes minimal assumptions regarding the structure or statistics of the photo collection. We present several variants of an automatic unsupervised algorithm to partition a collection of digital photographs based either on temporal similarity alone, or on temporal and content-based similarity. First, inter-photo similarity is quantified at multiple temporal scales to identify likely event clusters. Second, the final clusters are determined according to one of three clustering goodness criteria. The clustering criteria trade off computational complexity and performance. We also describe a supervised clustering method based on learning vector quantization. Finally, we review the results of an experimental evaluation of the proposed algorithms and existing approaches on two test collections.
Publication Details
  • International Conference on Image and Video Retrieval 2005
  • Jul 21, 2005

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Large video collections present a unique set of challenges to the search system designer. Text transcripts do not always provide an accurate index to the visual content, and the performance of visually based semantic extraction techniques is often inadequate for search tasks. The searcher must be relied upon to provide detailed judgment of the relevance of specific video segments. We describe a video search system that facilitates this user task by efficiently presenting search results in semantically meaningful units to simplify exploration of query results and query reformulation. We employ a story segmentation system and supporting user interface elements to effectively present query results at the story level. The system was tested in the 2004 TRECVID interactive search evaluations with very positive results.