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Video Hyperlinking Tutorial (Part C)

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Authors/Presenters: Vasileios Mezaris and Benoit Huet.
Video hyperlinking is the introduction of links that originate from pieces of video material and point to other relevant content, be it video or any other form of digital content. The tutorial presents the state of the art in video hyperlinking approaches and in relevant enabling technologies, such as video analysis and multimedia indexing and retrieval. Several alternative strategies, based on text, visual and/or audio information are introduced, evaluated and discussed, providing the audience with details on what works and what doesn’t on real broadcast material.

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Video Hyperlinking Tutorial (Part C)

  1. 1. Information Technologies Institute Centre for Research and Technology Hellas Video Hyperlinking Part C: Insights into Hyperlinking Video Content Benoit Huet EURECOM (Sophia-Antipolis, France) IEEE ICIP’14 Tutorial, Oct. 2014 ACM MM’14 Tutorial, Nov. 2014
  2. 2. Information Technologies Institute 3.2 Centre for Research and Technology Hellas Overview • Introduction – overall motivation • The General Framework • Indexing Video for Hyperlinking – Apache Solr • Evaluation Measures • Challenge 1: Temporal Granularity – Feature Alignment and Index Granularity • Challenge 2: Crafting the Query – Selecting Keywords – Selecting Visual Concepts • Hyperlinking Evaluation: MediaEval S&H • Hyperlinking Demos and LinkedTV Video • Conclusion and Outlook • Additional Reading
  3. 3. Information Technologies Institute 3.3 Centre for Research and Technology Hellas Motivation • Why Video Hyperlinking? – Linking multimedia documents with related content – Automatic Hyperlink Creation • Different from Search (no user query) • Query automatically crafted from source document content • Outreach – Recommendation system – Second screen applications
  4. 4. Information Technologies Institute 3.4 Centre for Research and Technology Hellas Insights in Hyperlinking • Hyperlinking – Creating “links” between media •Video Hyperlinking –video to video –video fragment to video fragment
  5. 5. Information Technologies Institute 3.5 Centre for Research and Technology Hellas Characterizing - Video • Video – Title / Episode – Cast – Synopsis / Summary – Broadcast channel – Broadcast date – URI – Named Entities
  6. 6. Information Technologies Institute 3.6 Centre for Research and Technology Hellas Characterizing – Video Fragment • Video Fragment – Temporal location (Start and End) – Subtitles / Transcripts – Named Entities – Visual Concepts – Events – OCR – Character / Person
  7. 7. Information Technologies Institute 3.7 Centre for Research and Technology Hellas General framework Video Dataset Segmentation Feature Extraction Indexing Video Anchor Fragment Feature Selection Retrieval Personalisation • Index Creation •Hyperlinking
  8. 8. Information Technologies Institute 3.8 Centre for Research and Technology Hellas Search and Hyperlinking Framework BroadCast Media Metadata (Subtitles,..) Lucene/Solr Media DB Solr Index Content Analysis Title Cast Channel Subtitles Transcript 1 Transcript 2 … Shots Scene OCR Visual concepts
  9. 9. Information Technologies Institute 3.9 Centre for Research and Technology Hellas Indexing Video for Hyperlinking • Indexing systems: – Apache Lucene/Solr – TerrierIR – ElasticSearch – Xapian – … • Popular for text-based indexing/search/retrieval • How to use index video for hyperlinking?
  10. 10. Information Technologies Institute 3.10 Centre for Research and Technology Hellas Solr Indexing • Solr engine (Apache Lucene) for data indexing – Index at different temporal granularities (shot, scene, sliding window) – Index different features at each temporal granularity (metadata, ocr, transcripts, visual concepts) • All information stored in a unified structured way – flexible tool to perform search and hyperlinking http://lucene.apache.org/solr/
  11. 11. Information Technologies Institute 3.11 Centre for Research and Technology Hellas Solr indexing – Sample Schema • Schema = structure of document using fields of different types • Fields: – name – Type (see next slide) – indexed=“true|false” – stored=“true|false” – multiValued=“true|false" – required=“true|false"
  12. 12. Information Technologies Institute 3.12 Centre for Research and Technology Hellas Solr indexing – Sample Schema • Fields type: – text (analysed, stopword removal, etc…) – string (not analysed) – date – float – int • uniqueKey – unique document id
  13. 13. Information Technologies Institute 3.13 Centre for Research and Technology Hellas Solr indexing – Sample Schema <?xml version="1.0" encoding="UTF-8" ?> <schema name="subtitles" version="1.5"> <fields> <field name="videoId" type="string" indexed="true" stored="true" multiValued="false" required="true"/> <field name="serie_title" type="text_ws" indexed="false" stored="true" multiValued="false" required="true" /> <field name="short_synopsis" type="text_en_splitting" indexed="false" stored="true" multiValued="false" required="true" /> <field name="episode_title" type="text_en_splitting" indexed="false" stored="true" multiValued="false" required="true" /> <field name="channel" type="text_ws" indexed="false" stored="true" multiValued="false" required="true" /> <field name="cast" type="text_en_splitting" indexed="false" stored="true" multiValued="false" required="true" /> <field name="description" type="text_en_splitting" indexed="false" stored="true" multiValued="false" required="true" /> <field name="synopsis" type="text_en_splitting" indexed="false" stored="true" multiValued="false" required="true"/> <field name="subtitle" type="text_en_splitting" indexed="true" stored="true" multiValued="false" required="true"/> <field name="duration" type="int" indexed="false" stored="true" multiValued="false" required="true"/> <field name="shots_number" type="int" indexed="false" stored="true" multiValued="false" required="true"/> <field name="text" type="text_en_splitting" indexed="true" stored="false" multiValued="true" required="true"/> <field name="names" type="text_ws" indexed="true" stored="false" multiValued="true" required="true"/> <field name="keywords" type="text_ws" indexed="true" stored="false" multiValued="true" required="true"/> <field name="_version_" type="long" indexed="true" stored="true"/> </fields> <uniqueKey>videoId</uniqueKey> …
  14. 14. Information Technologies Institute 3.14 Centre for Research and Technology Hellas Solr Indexing – Sample Document <?xml version="1.0" encoding="UTF-8"?> <add> <doc> <field name="videoId">20080506_183000_bbcfour_pop_goes_the_sixties</field> <field name="subtitle">SCREAMING APPLAUSE Subtitles by Red Bee Media Ltd E-mail subtitling@bbc.co.uk HELICOPTER WHIRRS TRAIN SPEEDS SIREN WAILS ENGINE REVS Your town, your street, your home - it's all in our database. New technology means it's easyto pay your TV licence and impossible to hide if you don't. KNOCKING</field> <field name="serie_title">Pop Goes the Sixties</field> <field name="short_synopsis">A colourful nugget of pop by The Shadows, mined from the BBC's archive.</field> <field name="description">The Shadows play their song Apache in a classic performance from the BBC's archives.</field> <field name="duration">300</field> <field name="episode_title">The Shadows</field> <field name="channel">BBC Four</field> <field name="cast" /> <field name="synopsis" /> <field name="shots_number">14</field> <field name="keywords">SCREAMING SPEEDS HELICOPTER WHIRRS REVS KNOCKING WAILS ENGINE SIREN APPLAUSE TV TRAIN Ltd E-mail Bee Subtitles Media Red</field> </doc> </add>
  15. 15. Information Technologies Institute 3.15 Centre for Research and Technology Hellas Solr Indexing • Analysis step: – Dependent on each type – Automatically performed: tokenization, removing stop words, etc… – It creates tokens that are added to the index • inverted index • query is made on tokens
  16. 16. Information Technologies Institute 3.16 Centre for Research and Technology Hellas Solr Query • Very easy with web interface
  17. 17. Information Technologies Institute 3.17 Centre for Research and Technology Hellas Indexing Video Fragments with Solr • Demo DEMO
  18. 18. Information Technologies Institute 3.18 Centre for Research and Technology Hellas Solr Query • Very easy with web interface • Query can be made through http request – http://localhost:8983/solr/collection_mediaEval/select?q=text:(Children out on poetry trip Exploration of poetry by school children Poem writing)
  19. 19. Information Technologies Institute 3.19 Centre for Research and Technology Hellas Evaluation measures • Search – Mean Reciprocal Rank (MRR): assesses the rank of the relevant segment
  20. 20. Information Technologies Institute 3.20 Centre for Research and Technology Hellas Evaluation measures • Search – Mean Reciprocal Rank (MRR): assesses the rank of the relevant segment – Mean Generalized Average Precision (mGAP): takes into account starting time of the segment – Mean Average Segment Precision (MASP): measures both ranking and segmentation of relevant segments
  21. 21. Information Technologies Institute 3.21 Centre for Research and Technology Hellas Evaluation measures • Hyperlinking – Precision at rank n: how many relevant segment appear in the top n results – Mean Average Precision (MAP) – taking temporal segment to target offset into account Aly, R., Ordelman, R. J.F., Eskevich, M., Jones, G. J.F., Chen, S. Linking Inside a Video Collection - What and How to Measure? In Proceedings of ACM WWW International Conference on World Wide Web Companion. ACM, Rio de Janeiro, Brazil, 457-460.
  22. 22. Information Technologies Institute 3.22 Centre for Research and Technology Hellas Challenge 1: Temporal Granularity Content Analysis BroadCast Media Metadata (Subtitles,..) Lucene/Solr Media DB Solr Index Program level: title, cast,… Audio-frame level: transcripts, subtitles… Shot/Keyframe level: visual concepts, OCR
  23. 23. Information Technologies Institute 3.23 Centre for Research and Technology Hellas Challenge 1: Temporal Granularity • Aligning features with different temporal granularity – Shots and Scenes – Aligned by construction Subtitles Shots Scenes
  24. 24. Information Technologies Institute 3.24 Centre for Research and Technology Hellas Challenge 1: Temporal Granularity • Aligning features with different temporal granularity – Subtitles and Scenes – CONFLICT! Subtitles Shots Scenes
  25. 25. Information Technologies Institute 3.25 Centre for Research and Technology Hellas Challenge 1: Temporal Granularity • Aligning features with different temporal granularity – Subtitles and Scenes – Alignment based on feature start Subtitles Shots Scenes
  26. 26. Information Technologies Institute 3.26 Centre for Research and Technology Hellas Challenge 1: Temporal Granularity • Aligning features with different temporal granularity – Subtitles and Scenes – Alignment based on feature end Subtitles Shots Scenes
  27. 27. Information Technologies Institute 3.27 Centre for Research and Technology Hellas Challenge 1: Temporal Granularity • Aligning features with different temporal granularity – Subtitles and Scenes – Feature duplication (bias?) Subtitles Shots Scenes
  28. 28. Information Technologies Institute 3.28 Centre for Research and Technology Hellas Challenge 1: Temporal Granularity • Aligning features with different temporal granularity – Subtitles and Scenes – Alignment based on temporal overlap Subtitles Shots Scenes > <
  29. 29. Information Technologies Institute 3.29 Centre for Research and Technology Hellas Performance Impact - Alignment Scene-Subtitle-End Scene-Subtitle-Begin Scene-Subtitle-Duplicate Scene-Subtitle-Overlap
  30. 30. Information Technologies Institute 3.30 Centre for Research and Technology Hellas Performance Impact - Granularity
  31. 31. Information Technologies Institute 3.31 Centre for Research and Technology Hellas Challenge 1: Discussion • Subtitle to scene Alignment: – Similar performance across approaches – Slight advantage to align using segment start • Granularity Impact – Shots are too short – Scenes better reflect user’s requirements
  32. 32. Information Technologies Institute 3.32 Centre for Research and Technology Hellas Let’s Hyperlink! Content Analysis BroadCast Media Metadata (Subtitles,..) Lucene/Solr Media DB Solr Index <anchor> <anchorId>anchor_1</anchorId> <fileName>v20080511_203000_bbctwo_TopGear</fileName> <startTime>13.07</startTime> <endTime>14.03</endTime> </anchor>
  33. 33. Information Technologies Institute 3.33 Centre for Research and Technology Hellas Challenge 2 : Crafting the Query Content Analysis BroadCast Media Metadata (Subtitles,..) Lucene/Solr Media DB Solr Index <anchor> <anchorId>anchor_1</anchorId> <fileName>v20080511_203000_bbctwo_TopGear</fileName> <startTime>13.07</startTime> <endTime>14.03</endTime> </anchor> Query crafted from the anchor Extract text from subtitles aligned with the anchor Identify relevant visual concepts from the subtitles Select visual concepts occurring in the anchor
  34. 34. Information Technologies Institute 3.34 Centre for Research and Technology Hellas Challenge 2a : Keyword Selection • Long anchor may generate long text query • Important Keyword (or Entities) should be favored
  35. 35. Information Technologies Institute 3.35 Centre for Research and Technology Hellas Challenge 2a : Keyword Selection • Keyword extraction based on term frequency-inverse document frequency (TF IDF) approach • IDF computed on English news, with curated stop words (~200 entries) • Incorporates Snowball stemming (as part of the Lucene project) • 50 weighted keywords per documents, singletons removed • Keyword Gluing for frequencies larger than 2 S. Tschöpel and D. Schneider. A lightweight keyword and tag-cloud retrieval´algorithm for automatic speech recognition transcripts. In Proc. ISCA, 2010, Japan.
  36. 36. Information Technologies Institute 3.36 Centre for Research and Technology Hellas Keyword Selection Performance
  37. 37. Information Technologies Institute 3.37 Centre for Research and Technology Hellas Challenge 2b: Visual concept generality Content Analysis BroadCast Media Metadata (Subtitles,..) Lucene/Solr Media DB Solr Index No training data for visual concepts Use 151 visual concept detectors trained on TrecVid
  38. 38. Information Technologies Institute 3.38 Centre for Research and Technology Hellas 151 Visual Concepts (TrecVid 2012) • 3_Or_More_People • Actor • Adult • Adult_Female_Human • Adult_Male_Human • Airplane • Airplane_Flying • Airport_Or_Airfield • Anchorperson • Animal • Animation_Cartoon • Armed_Person • Athlete • Baby • Baseball • Basketball • Beach • Bicycles • Bicycling • Birds • Boat_Ship • Boy • Building • Bus • Car • Car_Racing • Cats • Cattle • Chair • Charts • Child • Church • City • Cityscape • Classroom • Clouds • Construction_Vehicles • Court • Crowd • Dancing • Daytime_Outdoor • Demonstration_Or_Protest • Desert • Dogs • Emergency_Vehicles • Explosion_Fire • Face • Factory • Female-Human-Face-Closeup • Female_Anchor • Female_Human_Face • Female_Person • Female_Reporter • Fields • Flags • Flowers • Football • Forest • Girl • Golf • Graphic • Greeting • Ground_Combat • Gun • Handshaking • Harbors • Helicopter_Hovering • Helicopters • Highway • Hill • Hockey • Horse • Hospital • Human_Young_Adult • Indoor • Insect • Kitchen • Laboratory • Landscape • Machine_Guns • Male-Human-Face-Closeup • Male_Anchor • Male_Human_Face • Male_Person • Male_Reporter • Man_Wearing_A_Suit • Maps • Meeting • …
  39. 39. Information Technologies Institute 3.39 Centre for Research and Technology Hellas Solr Query • How to include the visual concepts in Solr? – Using float typed fields – <field name=“Animal" type=“float" indexed="true" stored=“true" multiValued=“false" required="true"/> – <field name=“Animal">0.74</field> – <field name=“Building">0.12</field> • Query can be made through http request – http://localhost:8983/solr/collection_mediaEval/select?q=text:(cow+in+a+farm)+Animal:[0.5+TO+1] +Building:[0.2+TO+1]
  40. 40. Information Technologies Institute 3.40 Centre for Research and Technology Hellas Challenge 2b: Visual concept detectors confidence Content Analysis BroadCast Media Metadata (Subtitles,..) Lucene/Solr Media DB Solr Index No training data for visual concepts Use 151 visual concept detectors trained on TrecVid Unknown performance
  41. 41. Information Technologies Institute 3.41 Centre for Research and Technology Hellas Challenge 2b: Visual concept detector confidence • 100 top images for the concept “Animal” • 58 out of 100 are manually evaluated as valid • Confidence w = 0,58         
  42. 42. Information Technologies Institute 3.42 Centre for Research and Technology Hellas Challenge 2c: Map keywords to visual concepts Farm Shells Exploration Poem Animal House Memories Animal Birds Insect Cattle Dogs Building School Church Flags Mountain WordNet Mapping keywords visual concepts
  43. 43. Information Technologies Institute 3.43 Centre for Research and Technology Hellas Mapping keywords to visual concepts • Concepts mapped to the keyword "Castle” • Semantic similarity computed using the “Lin” distance Concept Windows Plant Court Church Building β 0.4533 0.4582 0.5115 0.6123 0.701
  44. 44. Information Technologies Institute 3.44 Centre for Research and Technology Hellas Fusing Text and Visual Scores Text-based scores Lucene indexing Visual-based scores WordNet similarity Selected concepts Ranking Fusion One score for each scene (t) fi=tiα +vi1−α One score for each scene (v): Computed from the scores of the selected concepts for each scene viq=wc×vsicc∈C'qΣ
  45. 45. Information Technologies Institute 3.45 Centre for Research and Technology Hellas Challenge 2c: Performance Results • Low impact of visual concept detector confidence (w) • Significant improvement can be achieved by combining only mapped concepts with θ ≥ 0.3. • Best performance is obtained when θ ≥ 0.8 (gain ≈ 11-12%). w=1.0 w=confidence(c) B. Safadi, M. Sahuguet and B. Huet, When textual and visual information join forces for multimedia retrieval, ICMR 2014, April 1-4, 2014, Glasgow, Scotland
  46. 46. Information Technologies Institute 3.46 Centre for Research and Technology Hellas Challenge 2d: Visual Concept Selection • 151 Visual Concept scores characterize each shots • Anchors may refer to 1 or more shots • Selection of relevant shots for the anchors using a threshold • For those selected visual concepts identify a good search threshold
  47. 47. Information Technologies Institute 3.47 Centre for Research and Technology Hellas Visual Concept Selection Performance • MAP Solr queriesConcepts selection 0.10.20.30.40.50.60.70.80.90.10.08920.03160.05580.08420.11830.1680.19140.19190.18980.20.17410.13660.11520.13120.15030.17770.19220.19190.18980.30.1840.18190.18060.16520.17310.18480.19270.19190.18980.40.18740.18830.19140.18680.18890.18970.19370.19190.18980.50.18750.18740.18860.19280.19370.18960.19390.19190.18980.60.18920.18840.18860.19130.19310.19460.19520.19230.18980.70.19010.19010.19010.1910.19170.19430.19480.19050.18910.80.19350.19350.19350.19430.19470.19590.19540.19640.190.90.19460.19460.19460.19520.19530.19620.19610.19580.1945
  48. 48. Information Technologies Institute 3.48 Centre for Research and Technology Hellas Visual Concept Selection Performance
  49. 49. Information Technologies Institute 3.49 Centre for Research and Technology Hellas Visual Concept Selection Performance • Precision@5 Solr queriesConcepts selection 0.10.20.30.40.50.60.70.80.90.10.55330.260.31330.460.54670.660.70.73330.73330.20.720.66670.52670.62670.640.70.70670.73330.73330.30.68670.720.70670.64670.70.72670.70670.73330.73330.40.70.70.72670.69330.71330.74670.71330.73330.73330.50.71330.71330.70670.720.740.740.71330.73330.73330.60.72670.72670.72670.73330.73330.740.71330.73330.73330.70.720.720.720.72670.73330.73330.71330.73330.73330.80.740.740.740.740.740.75330.74670.740.740.90.740.740.740.740.740.75330.75330.75330.74
  50. 50. Information Technologies Institute 3.50 Centre for Research and Technology Hellas Visual Concept Selection Performance
  51. 51. Information Technologies Institute 3.51 Centre for Research and Technology Hellas Visual Concept Selection Performance • Precision@10 Solr queriesConcepts selection 0.10.20.30.40.50.60.70.80.90.10.40330.16670.23330.32330.43670.550.60330.61670.62670.20.57330.50.430.49670.510.57330.60670.61670.62670.30.60330.57330.57670.570.55670.59670.60670.61670.62670.40.590.58670.60.590.60.60670.60670.61670.62670.50.590.590.59670.60.590.60.610.61670.62670.60.610.610.610.610.60670.59330.610.61330.62670.70.610.610.610.610.610.59670.61330.61330.62330.80.61670.61670.61670.620.62330.61330.62330.62670.62330.90.630.630.630.63330.63330.630.63670.63670.6333
  52. 52. Information Technologies Institute 3.52 Centre for Research and Technology Hellas Visual Concept Selection Performance
  53. 53. Information Technologies Institute 3.53 Centre for Research and Technology Hellas Visual Concept Selection Performance • Precision@20 Solr queriesConcepts selection 0.10.20.30.40.50.60.70.80.90.10.26830.1050.170.22670.30330.40170.440.44830.440.20.41670.3450.30330.33830.39330.43170.440.44830.440.30.4350.43330.43170.4050.42330.44170.440.44830.440.40.44330.43670.44330.44330.44330.44330.44170.44830.440.50.4450.44170.44170.44670.45830.44830.44170.44830.440.60.44670.4450.4450.450.45670.44830.44170.44830.440.70.45330.45330.45330.4550.45830.45830.44170.44830.43830.80.45170.45170.45170.45170.45330.45170.4450.44830.440.90.450.450.450.450.450.44830.44830.44830.4483
  54. 54. Information Technologies Institute 3.54 Centre for Research and Technology Hellas Visual Concept Selection Performance
  55. 55. Information Technologies Institute 3.55 Centre for Research and Technology Hellas Challenge 2e: Combining Visual Concept Selection and Fusion • Logic (AND/OR) vs Fusion (weighted sum) • Text vs Visual Concepts weight • Visual Concept selection threshold
  56. 56. Information Technologies Institute 3.56 Centre for Research and Technology Hellas Challenge 2e: Combining Visual Concept Selection and Fusion • MAP Text vs Visual concept weight Visual Concept Selection Threshold 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0,1 0,227 0,232 0,233 0,233 0,233 0,233 0,233 0,233 0,232 0,2 0,206 0,228 0,23 0,231 0,232 0,231 0,231 0,231 0,233 0,3 0,185 0,219 0,225 0,227 0,228 0,228 0,229 0,23 0,232 0,4 0,168 0,21 0,22 0,225 0,227 0,228 0,229 0,23 0,232 0,5 0,138 0,201 0,215 0,221 0,223 0,226 0,226 0,23 0,231 0,6 0,138 0,199 0,213 0,219 0,223 0,225 0,227 0,23 0,232 0,7 0,132 0,197 0,213 0,219 0,223 0,228 0,229 0,232 0,233 0,8 0,091 0,139 0,169 0,186 0,196 0,204 0,213 0,222 0,231 0,9 0,195 0,206 0,213 0,218 0,22 0,221 0,224 0,228 0,231
  57. 57. Information Technologies Institute 3.57 Centre for Research and Technology Hellas Challenge 2e: Combining Visual Concept Selection and Fusion 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0,08 0,1 0,12 0,14 0,16 0,18 0,2 0,22 0,24 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 Text vs Visual Concept Fusion Weight Visual Concept Selection Threshold MAP 0,22-0,24 0,2-0,22 0,18-0,2 0,16-0,18 0,14-0,16 0,12-0,14 0,1-0,12 0,08-0,1
  58. 58. Information Technologies Institute 3.58 Centre for Research and Technology Hellas Challenge 2: Discussion • Keyword selection is important • Mapping text with visual concepts isn’t straight forward – But can boost performance • Visual concept detector confidence has limited effect on performance • Selecting visual concepts from the anchor is easier that mapping from text
  59. 59. Information Technologies Institute 3.59 Centre for Research and Technology Hellas Hyperlinking Evaluation • Evaluate LinkedTV / MediaMixer Technologies for Analysing and Connecting together video fragments with related content • Relevance to users • Large-scale video collection MediaEval Benchmarking Initiative for Multimedia Evaluation The "multi" in multimedia: speech, audio, visual content, tags, users, context
  60. 60. Information Technologies Institute 3.60 Centre for Research and Technology Hellas The MediaEval Search and Hyperlinking Task • Information seeking in a video dataset: retrieving video/media fragments Eskevich, M., Aly, R., Ordelman, R., Chen, S., Jones, G. J.F. The Search and Hyperlinking Task at MediaEval 2013. In Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop, CEUR-WS.org, 1043, ISSN: 1613-0073. Barcelona, Spain, 2013.
  61. 61. Information Technologies Institute 3.61 Centre for Research and Technology Hellas The MediaEval Search and Hyperlinking Task • The 2013 dataset: 2323 BBC videos of different genres (440 programs)
  62. 62. Information Technologies Institute 3.62 Centre for Research and Technology Hellas The MediaEval Search and Hyperlinking Task • The 2013 dataset: 2323 BBC videos of different genres (440 programs) – ~1697h of video + audio – Two types of ASR transcript (LIUM/LIMSI) – Manual subtitle – Metadata (channel, cast, synopsis, etc…) – Shot boundaries and keyframes – Face detection and similarity information – Concept detection
  63. 63. Information Technologies Institute 3.63 Centre for Research and Technology Hellas The 2013 MediaEval Search and Hyperlinking Task • Search: find a known segment in the collection given a query (text) <top> <itemId>item_18</itemId> <queryText>What does a ball look like when it hits the wall during Squash</queryText> <visualCues>ball hitting a wall in slow motion</visualCues> </top> • Hyperlinking: find relevant segments relatively to an “anchor” segment (+- context) <anchor> <anchorId>anchor_1</anchorId> <startTime>13.07</startTime> <endTime>13.22</endTime> <item> <fileName>v20080511_203000_bbcthree_little_britain</fileName> <startTime>13.07</startTime> <endTime>14.03</endTime> </item> </anchor>
  64. 64. Information Technologies Institute 3.64 Centre for Research and Technology Hellas The 2013 MediaEval Search and Hyperlinking Task • Queries are user generated for both search and hyperlinking – Search: 50 queries from 29 users • Known-item: the target is known to be in the dataset – Hyperlinking: 98 anchors • Evaluation: – For search, searched segments are pre-defined – For hyperlinking, crowd-sourcing – (on 30 anchors only)
  65. 65. Information Technologies Institute 3.65 Centre for Research and Technology Hellas MediaEval 2013 Submissions • Search Runs: – scenes-S(-U,-I): scenes search using only textual features from subtitles (I and U: transcript type) – scenes-noC (-C): scenes search using textual (and visual) features – cl10-noC (-C) : temporal shot clustering within a video using textual features (and visual cues).
  66. 66. Information Technologies Institute 3.66 Centre for Research and Technology Hellas Search Results • Best performance obtained with scenes • Impact of visual concept: smaller than expected Run MRR mGAP MASP scenes-C 0.324931 0.187194 0.199647 scenes-noC 0.324603 0.186916 0.199237 scenes-S 0.338594 0.182194 0.210934 scenes-I 0.261996 0.144708 0.158552 scenes-U 0.268045 0.152094 0.164817 cl10-C 0.294770 0.154178 0.181982 cl10-noC 0.286806 0.149530 0.171888
  67. 67. Information Technologies Institute 3.67 Centre for Research and Technology Hellas mGAP results (60s window)
  68. 68. Information Technologies Institute 3.68 Centre for Research and Technology Hellas Example Search and Result • Text query : what to cook with everyday ingredients on a budget, denise van outen, john barrowman, ainsley harriot, seabass, asparagus,ostrich, mushrooms, sweet potato, mango, tomatoes • Visual cues: denise van outen, john barrowman, ainsley harriot, seabass, asparagus,ostrich, mushrooms, sweet potato, mango, tomatoes Expected Anchor 20080506_153000_bbctwo_ready_steady_cook.webm#t=67,321 Scenes 20080506_153000_bbctwo_ready_steady_cook.webm#t=48,323 cl10 20080506_153000_bbctwo_ready_steady_cook.webm#t=1287,1406
  69. 69. Information Technologies Institute 3.69 Centre for Research and Technology Hellas MediaEval 2013 Submissions • Hyperlinking Runs: – LA-scenes (-cl10/-MLT): only information from the anchor is used – LC-scenes (-cl10/-MLT): a segment containing the anchor is used (context)
  70. 70. Information Technologies Institute 3.70 Centre for Research and Technology Hellas 2013 Hyperlinking Results • Scenes offer the best results • Using context (LC) improves performances • Precision at rank n decreases with n Run MAP P-5 P-10 P-20 LA cl10 0.0337 0.3467 0.2533 0.1517 LA MLT 0.1201 0.4200 0.4200 0.3217 LA scenes 0.1196 0.6133 0.5133 0.3400 LC cl10 0.0550 0.4600 0.4000 0.2167 LC MLT 0.1820 0.5667 0.5667 0.4300 LC scenes 0.1654 0.6933 0.6367 0.4333
  71. 71. Information Technologies Institute 3.71 Centre for Research and Technology Hellas 2013 Hyperlinking Results (P=10 - 60s windows)
  72. 72. Information Technologies Institute 3.72 Centre for Research and Technology Hellas The Search and Hyperlinking Demo Content Analysis BroadCast Media Metadata (Subtitles) Lucene/Solr Media DB Solr Index WebService (HTML5/AJAX/PHP) User Interface
  73. 73. Information Technologies Institute 3.73 Centre for Research and Technology Hellas • LinkedTV hyperlinking scenario Demonstration
  74. 74. Information Technologies Institute 3.74 Centre for Research and Technology Hellas Conclusions and Outlook • Scenes offer the best temporal granularity • Actual algorithm based on visual features only • Future work: including semantic and audio features • Importance of Context • Visual features integration is challenging • Visual concept detectors (accuracy and coverage) • Combination of multimodal features • Mapping between text/entities and visual concepts • Person identification
  75. 75. Information Technologies Institute 3.75 Centre for Research and Technology Hellas Contributors • Mrs Mathilde Sahuguet (EURECOM/DailyMotion) • Dr. Bahjat Safadi (EURECOM) • Mr Hoang-An Le (EURECOM) • Mr Quoc-Minh Bui (EURECOM) • LinkedTV Partners (CERTH/ITI, UEP, Fraunhofer IAIS)
  76. 76. Information Technologies Institute 3.76 Centre for Research and Technology Hellas Additional Reading • E. Apostolidis, V. Mezaris, M. Sahuguet, B. Huet, B. Cervenkova, D. Stein, S. Eickeler, J.-L. Redondo Garcia, R. Troncy, L. Pikora, "Automatic fine-grained hyperlinking of videos within a closed collection using scene segmentation", Proc. ACM Multimedia (MM'14), Orlando, FL, US, 3-7 Nov. 2014. • B. Safadi, M. Sahuguet and B. Huet, When textual and visual information join forces for multimedia retrieval, ICMR 2014, ACM International Conference on Multimedia Retrieval, April 1-4, 2014, Glasgow, Scotland • M. Sahuguet and B. Huet. Mining the Web for Multimedia-based Enriching. Multimedia Modeling MMM 2014, 20th International Conference on MultiMedia Modeling, 8-10th January 2014, Dublin, Ireland • M. Sahuguet, B. Huet, B. Cervenkova, E. Apostolidis, V. Mezaris, D. Stein, S. Eickeler, J-L. Redondo Garcia, R. Troncy, L. Pikora. LinkedTV at MediaEval 2013 search and hyperlinking task, MEDIAEVAL 2013, Multimedia Benchmark Workshop, October 18-19, 2013, Barcelona, Spain • Stein, D.; Öktem, A.; Apostolidis, E.; Mezaris, V.; Redondo García, J. L.; Troncy, R.; Sahuguet, M. & Huet, B., From raw data to semantically enriched hyperlinking: Recent advances in the LinkedTV analysis workflow, NEM Summit 2013, Networked & Electronic Media, 28-30 October 2013, Nantes, France • W. Bailer, M. Lokaj, and H. Stiegler. Context in video search: Is close-by good enough when using linking? In ACM ICMR, Glasgow, UK, April 1-4 2014. • C. A. Bhatt, N. Pappas, M. Habibi, et al. Multimodal reranking of content-based recommendations for hyperlinking video snippets. In ACM ICMR, Glasgow, UK, April 1-4 2014. • D. Stein, S. Eickeler, R. Bardeli, et al. Think before you link! Meeting content constraints when linking television to the web. In NEM Summit 2013, 28-30, October 2013, Nantes, France. • P. Over, G. Awad, M. Michel, et al. TRECVID 2012 An overview of the goals, tasks, data, evaluation mechanisms and metrics. In Proc. of TRECVID 2012. NIST, USA, 2012. • M. Eskevich, G. Jones, C. Wartena, M. Larson, R. Aly, T. Verschoor, and R. Ordelman. Comparing retrieval effectiveness of alternative content segmentation methods for Internet video search. In Content-Based Multimedia Indexing (CBMI), 2012.
  77. 77. Information Technologies Institute 3.77 Centre for Research and Technology Hellas Additional Reading • Lei Pang, Wei Zhang, Hung-Khoon Tan, and Chong-Wah Ngo. 2012. Video hyperlinking: libraries and tools for threading and visualizing large video collection. In Proceedings of the 20th ACM international conference on Multimedia (MM '12). ACM, New York, NY, USA, 1461-1464. • A. Habibian, K. E. van de Sande, and C. G. Snoek. Recommendations for Video Event Recognition Using Concept Vocabularies. In Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval, ICMR ’13, pages 89–96, Dallas, Texas, USA, April 2013. • A. Hauptmann, R. Yan, W.-H. Lin, M. Christel, and H. Wactlar. Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News. Multimedia, IEEE Transactions on, 9(5):958–966, 2007. • A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12):1349–1380, 2000. • A. Rousseau, F. Bougares, P. Deleglise, H. Schwenk, and Y. Estev. LIUM's systems for the IWSLT 2011 Speech Translation Tasks. In Proceedings of IWSLT 2011, San Francisco, USA, 2011. • Gauvain, J.-L., Lamel, L. and Adda, G., 2002. The LIMSI broadcast news transcription system. Speech Communication 37, 89- 108 • C. Fellbaum, editor. WordNet: an electronic lexical database. MIT Press, 1998. • Carles Ventura, Marcel Tella-Amo, Xavier Giro-I-Nieto, “UPC at MediaEval 2013 Hyperlinking Task”, Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop, Barcelona, Spain, October 18-19, 2013. • Camille Guinaudeau, Anca-Roxana Simon, Guillaume Gravier, Pascale Sébillot, “HITS and IRISA at MediaEval 2013: Search and Hyperlinking Task” , Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop, Barcelona, Spain, October 18-19, 2013. • Mathilde Sahuguet, Benoit Huet, Barbora Červenková, Evlampios Apostolidis, Vasileios Mezaris, Daniel Stein, Stefan Eickeler, Jose Luis Redondo Garcia, Lukáš Pikora, “LinkedTV at MediaEval 2013 Search and Hyperlinking Task” , Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop, Barcelona, Spain, October 18-19, 2013.
  78. 78. Information Technologies Institute 3.78 Centre for Research and Technology Hellas Additional Reading • Tom De Nies, Wesley De Neve, Erik Mannens, Rik Van de Walle, “Ghent University-iMinds at MediaEval 2013: An Unsupervised Named Entity-based Similarity Measure for Search and Hyperlinking” , Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop, Barcelona, Spain, October 18-19, 2013. • Fabrice Souvannavong, Bernard Mérialdo, Benoit Huet, Video content modeling with latent semantic analysis, CBMI 2003, 3rd International Workshop on Content-Based Multimedia Indexing, September 22-24, 2003, Rennes, France • Itheri Yahiaoui, Bernard Merialdo, Benoit Huet, Comparison of multiepisode video summarization algorithms, EURASIP Journal on applied signal processing, 2003 • Chidansh Bhatt, Nikolaos Pappas, Maryam Habibi, Andrei Popescu-Belis, “Idiap at MediaEval 2013: Search and Hyperlinking Task” , Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop, Barcelona, Spain, October 18-19, 2013. • Petra Galuščáková, Pavel Pecina, “CUNI at MediaEval 2013 Search and Hyperlinking Task” , Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop, Barcelona, Spain, October 18-19, 2013. • Shu Chen, Gareth J.F. Jones, Noel E. O'Connor, “DCU Linking Runs at MediaEval 2013: Search and Hyperlinking Task” , Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop, Barcelona, Spain, October 18-19, 2013. • Michal Lokaj, Harald Stiegler, Werner Bailer, “TOSCA-MP at Search and Hyperlinking of Television Content Task” , Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop, Barcelona, Spain, October 18-19, 2013. • Bahjat Safadi, Mathilde Sahuguet, Benoit Huet, Linking text and visual concepts semantically for cross modal multimedia search, 21st IEEE International Conference on Image Processing, October 27-30, 2014, Paris, France Indexing Systems • http://lucene.apache.org/solr/ • http://terrier.org/ • http://www.elasticsearch.org/ • http://xapian.org Projects • LinkedTV: Television linked to the web. http://www.linkedtv.eu/ • MediaMixer: Community set-up and networking for the remixing of online media fragments. http://www.mediamixer.eu/ • Axes: Access to audiovisual archives. http://www.axes-project.eu
  79. 79. Information Technologies Institute 3.79 Centre for Research and Technology Hellas Thank you! More information: http://www.eurecom.fr/~huet benoit.huet@eurecom.fr

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