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Image and Video Retrieval and Visual Analytics: Opportunities for Collaboration

Image and Video Retrieval and Visual Analytics: Opportunities for Collaboration. Mike Christel, christel@cs.cmu.edu Entertainment Technology Center, Carnegie Mellon University. Talk Outline. (Heavy on demonstrations, light on slides….) Introduction to two test corpora on my demo laptop

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Image and Video Retrieval and Visual Analytics: Opportunities for Collaboration

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  1. Image and Video Retrieval and Visual Analytics: Opportunities for Collaboration Mike Christel, christel@cs.cmu.edu Entertainment Technology Center, Carnegie Mellon University

  2. Talk Outline (Heavy on demonstrations, light on slides….) Introduction to two test corpora on my demo laptop Very quick overview of CMU Informedia research Speech recognition; text alignment Image processing; visual concept classification Language processing; named entity detection Lessons Learned Opportunities

  3. CMU Informedia Video Research Details at: http://www.informedia.cs.cmu.edu Speech recognition and alignment; image processing; named entity tagging Synchronized metadata for search and navigation Fast, direct video access to oral histories, news, etc. Demonstration oral history corpus: 913 hours of interviews from 400 individuals, 18,254 interview story segments (average story segment length of 3 minutes) Demonstration news corpus: NIST TRECVID 2006 test set (165 hours of U.S., Arabic, and Chinese news with 79,484 reference shots)

  4. The HistoryMakers Oral History Archive http://www.thehistorymakers.com The world’s largest African American oral history archive with accomplished African Americans Purpose: To educate and show the breadth and depth of this important American history as told by the first person To highlight the accomplishments of individual African Americans across a variety of disciplines To preserve this material for generations to come Committed to exposing the archive to the widest audience possible, making use of new technologies as appropriate

  5. The HistoryMakers Intellectual Property • The set of 400 interviewees I will show today is in a corpus with planned growth to 5000 • The work is in beta test, with strict limitations on copying and distribution:“All content is the property of The HistoryMakers™: all proposed uses must be submitted in a proposal in advance to The HistoryMakers for approval before anything can be used and approval is totally at our discretion.” – Julieanna Richardson, Founder & Executive Director, The HistoryMakers, Chicago, IL

  6. A Theme for Today: User Involvement User Correction: Corrective action for metadata errors (analogous to Harry Shum’s vision at Microsoft for human-assisted computer vision success) User Control: Driving the interface to overcome metadata errors User Context: More useful interfaces driven implicitly by context

  7. Speech Recognition Functions Generates transcript (if one is not given) to enable text-based retrieval from spoken language documents Improves text synchronization to audio/video in presence of scripts (align speech with text) Supplies necessary information for library segmentation and multimedia abstractions (e.g., break stories apart at silence points rather than in the middle of sentences)

  8. Image Understanding Functions Scene segmentation Similarity matching Camera motion determination and object tracking Optical Character Recognition (OCR) on video text and titles Face detection and recognition Ongoing research work in object identification and scene characterization, e.g., indoor/outdoor, road, building, etc.

  9. Images Containing Similar Colors… Image search with tropical rainforest image leads to…

  10. Images Containing Similar Colors

  11. Images Containing Similar Shapes

  12. Images Containing Similar Content

  13. Goal: Automatic Video Characterization Static Static Zoom Adult Female Animal Two adults Head Motion Left Motion None CNN LIVE CNN An Online First Studio Outdoor Indoor Shots Yellowstone Camera Objects Action Captions Scenery

  14. Goal: Automatic Video Characterization Static Static Zoom Adult Female Animal Two adults Head Motion Left Motion None CNN LIVE CNN An Online First Studio Outdoor Indoor Shots Yellowstone Camera Objects Action Captions Scenery

  15. Automated Video Processing Produces descriptive metadata for video libraries Metadata has errors greater than metadata produced by a careful, human-provided annotation Errors in metadata can be reduced – examples to follow…

  16. Camera and Motion Detection Pan Right object motion (not pan left)

  17. Text and Face Detection

  18. Video OCR Block Diagram Text Area Detection Video Text Area Preprocessing Commercial OCR ASCII Text

  19. Video Frames Filtered Frames AND-ed Frames (1/2 sec. intervals)

  20. VOCR Preprocessing Problems

  21. Augmenting VOCR with Dictionary Look-up

  22. Named Entity Extraction F. Kubala, R. Schwartz, R. Stone, and R. Weischedel, “Named Entity Extraction from Speech”, Proc. DARPA Workshop on Broadcast News Understanding Systems, Lansdowne, VA, February 1998. CNN national correspondent John Holliman is at Hartsfield International Airport in Atlanta. Good morning, John. …But there was one situation here at Hartsfield where one airplane flying from Atlanta to Newark, New Jerseyyesterday had a mechanical problem and it caused a backup that spread throughout the whole system because even though there were a lot of planes flying to the New York area from the Atlanta area yesterday, …. Key: Place, Time, Organization/Person

  23. Improving the Interface via Usage Context Example: query-based thumbnail selection

  24. Improving Utility through End-User Control Example: filtering storyboard based on visual concepts with user controlling precision and recall

  25. Improving the Metadata via User Interaction Example: collecting positive and implicit negative sets of labeled shot data for visual concepts Reference: Ming-yu Chen, et al., ACM Multimedia 2005

  26. Automated Video Processing Produces descriptive metadata for video libraries Metadata has errors greater than metadata produced by a careful, human-provided annotation Errors in metadata can be reduced: By more computation-intensive algorithms By taking advantage of video frame-to-frame redundancy By folding in context, e.g., probable text sizes in video By folding in extra sources of knowledge, e.g., a dictionary for cleaning up VOCR, or labeled data revealing patterns for named entity detection By human review and correction, which can generate additional labeled data for machine learning

  27. Storyboards: TRECVID Search Success For the shot-based directed search information retrieval task evaluated at TRECVID, storyboards have consistently and overwhelmingly produced top scores Motivated users can navigate through thousands of shot thumbnails in storyboards, better even than with “extreme video retrieval” interfaces: 2487 shots on average per 15 minute topic for TRECVID 2006 (Christel & Yan, CIVR 2007) Storyboard benefits: packed visual overview, trivial interactive control needed for “overview, zoom and filter, details on demand” – Shneiderman’s Visual Information-Seeking Mantra

  28. Beyond Fact-Finding CACM (April 2006), Info. Processing and Mgt (March 2008), etc., have special issues on this topic G. Marchionini (“Exploratory Search: From Finding to Understanding,” CACM 49, April 2006) breaks down 3 types of search activities: Lookup (fact-finding; solving stated/understood need) Learn Investigate Computer scientists and information retrieval specialists emphasize evaluation of lookup activities (NIST TREC) Real world interest in learn/investigate: for an oral history collection, State Univ. New York at Buffalo Workshop library science and humanities participants quite interested in learn/investigate activities

  29. Exploratory Search Examples where storyboards still useful: visual review Where storyboards fail: Showing other facets like time, space, co-occurrence, named entities (When did disasters occur? Where?) Providing collection understanding, holistic view of what’s in 100s of segments of 1000s of shots Providing window into visually homogenous results, e.g., results from color search perhaps, or a corpus of just lectures, or head-and-shoulder interview shots Claim: Storyboards are not sufficient, but are part of a useful suite of tools/interfaces for interactive video search

  30. Anecdotal Support for Claim Collected 2006-2007 from: Government analysts with news data History students and faculty with oral history data Views Tested: Timeline Visual Info Browsing Environment (VIBE) Plot Map View Named Entity view (people, places, organizations) Text-dominant views: Nested Lists (pre-defined clusters by contributor) Common Text (on-the-fly grouping of common phrases)

  31. Anecdotal Results 38 HistoryMakers corpus users (mostly students, 15 female, average age 24), experienced web searchers, modest digital video experience 6 intelligence analysts (1 female; 2 older than 40, 3 in their 30s, 1 in 20s), very experienced text searchers, experienced web searchers, novice video searchers View use minimal aside from Common Text Text titling and text transcripts used frequently A bit of evidence for collection understanding (e.g., diffs in topic between New York and Chicago), but overall, cautious use of default settings for initial trial(s).

  32. Evaluation Hurdles How does one evaluate information visualization for promoting exploratory video search? Low level simple tasks vs. complex real-world tasks Traditional effectiveness, efficiency, satisfaction are even problematic: is “fast” interface for exploration good or bad? HCI discount usability techniques offer some support, but ecological validity may limit impact of conclusions (e.g., HCII students found Common Text well suited for History students) Look to HCI+Visual Analytics for help, e.g., Plaisant “First hour with system” studies, or “developer as user” insights too limiting. Rather, consider Multi-dimensional In-depth Long-term Case-studies (MILC)

  33. Reflections –Informedia Successes Open benchmarking to gauge progress in digital video libraries and video information retrieval NIST TREC Spoken Document Retrieval NIST TRECVID Application of machine learning techniques for visual classification; addressing “semantic gap” through visual concepts (Rong Yan, Wei-Hao Lin, Jun Yang, Robert Chen with Alex Hauptmann as PhD thesis advisor in LTI) User studies to empirically drive interface development (see http://www.morganclaypool.com/toc/icr/1/1 - Morgan & Claypool Synthesis Lectures on Information Concepts, Retrieval, and Services)

  34. Reflections – Opportunities Missed Foreseeing growth of the Web in 1994 Limited use and dissemination via work with broadcasters and intellectual property concerns Significant shifts in environment, e.g., from $1,000,000 for a terabyte of storage in 1994 to $100 (or less) today Emphasis on information retrieval in traditional sense (lookup tasks)

  35. Conclusions to Build Upon - 1 “Interactive” allows human direction to compensate for automation shortcomings and varying needs Interactive fact-finding better than automated fact-finding in visual shot retrieval (TRECVID) Interactive computer vision has successes (Harry Shum at Microsoft, Michael Brown et al. at NUS) Interactive view/facet control == ??? (too early to tell) Users need scaffolding/support to get started Evaluations need to run longer term, in depth, with case studies to see what has benefit (MILC)

  36. Conclusions to Build Upon - 2 Storyboards work well for visual overview Video surrogates can be made more effective, efficient, and satisfying when tailored to user activity (leverage context) Interface should provide easy tuning of precision vs. recall As cheap storage and transmission is producing a wealth of digital video, exploratory search will gain emphasis regarding video repositories Augment automatically produced metadata with human-provided descriptors (take advantage of what users are willing to volunteer, and in fact solicit additional feedback from humans through motivating games that allow for human computation, a research focus of Luis von Ahn at Carnegie Mellon University)

  37. Credits Many members of the Informedia Project, CMU research community, and The HistoryMakers contributed to this work, including: Informedia Project Director: Howard Wactlar The HistoryMakers Executive Director: Julieanna Richardson Informedia User Interface: Ron Conescu, Neema Moraveji Informedia Processing: Alex Hauptmann, Ming-yu Chen, Wei-Hao Lin, Rong Yan, Jun Yang Informedia Library Essentials: Bob Baron, Bryan Maher This work supported by the National Science Foundation under Grant Nos. IIS-0535056 and IIS-0705491

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