Video Hyperlinking Tutorial (Part C)

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

3.2 Information Technologies Institute 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

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

Insights in Hyperlinking

• Hyperlinking – Creating “links” between media

• Video Hyperlinking – video to video – video fragment to video fragment

Characterizing - Video

• Video – Title / Episode – Cast – Synopsis / Summary – Broadcast channel – Broadcast date – URI – Named Entities

Characterizing – Video Fragment

• Video Fragment – Temporal location (Start and End) – Subtitles / Transcripts – Named Entities – Visual Concepts – Events – OCR – Character / Person

General framework

Video Dataset Segmentation Feature Extraction Indexing

Video Anchor Fragment

Feature Selection Retrieval Personalisation

• Index Creation

• Hyperlinking

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

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?

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/

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"

• Fields type: – text (analysed, stopword removal, etc…) – string (not analysed) – date – float – int

• uniqueKey – unique document id

<?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> …

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>

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

Solr Query

• Very easy with web interface

Indexing Video Fragments with Solr

• Demo

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)

Evaluation measures

• Search – Mean Reciprocal Rank (MRR): assesses the rank

of the relevant segment

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

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.

Challenge 1: Temporal Granularity

Content Analysis

BroadCast Media

Media DB

Solr Index

Program level: title, cast,… Audio-frame level: transcripts, subtitles…

Shot/Keyframe level: visual concepts, OCR

• Aligning features with different temporal granularity – Shots and Scenes

– Aligned by construction

Subtitles Shots

Scenes

• Aligning features with different temporal granularity – Subtitles and Scenes

– CONFLICT!

Subtitles Shots

Scenes

– Alignment based on feature start

Subtitles Shots

Scenes

– Alignment based on feature end

Subtitles Shots

Scenes

– Feature duplication (bias?)

Subtitles Shots

Scenes

– Alignment based on temporal overlap

Subtitles Shots

Scenes

Performance Impact - Alignment

Scene-Subtitle-End Scene-Subtitle-Begin Scene-Subtitle-Duplicate Scene-Subtitle-Overlap

Performance Impact - Granularity

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

Let’s Hyperlink!

Content Analysis

BroadCast Media

Media DB

Solr Index

<anchor> <anchorId>anchor_1</anchorId> <fileName>v20080511_203000_bbctwo_TopGear</fileName> <startTime>13.07</startTime> <endTime>14.03</endTime> </anchor>

Challenge 2 : Crafting the Query

Content Analysis

BroadCast Media

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

Challenge 2a : Keyword Selection

• Long anchor may generate long text query • Important Keyword (or Entities) should be

favored

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.

Keyword Selection Performance

Challenge 2b: Visual concept generality

Content Analysis

BroadCast Media

Media DB

Solr Index

No training data for visual concepts

Use 151 visual concept detectors trained on TrecVid

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 • …

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/s

elect?q=text:(cow+in+a+farm)+Animal:[0.5+TO+1]+Building:[0.2+TO+1]

Challenge 2b: Visual concept detectors confidence

Content Analysis

BroadCast Media

Media DB

Solr Index

No training data for visual concepts

Use 151 visual concept detectors trained on TrecVid

Unknown performance

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

Challenge 2c: Map keywords to visual concepts

Shells

Exploration

Animal

Memories

Animal

Insect

Cattle

Building

School

Church

Mountain

WordNet Mapping ke

visual concepts

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

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 + vi

1−α

One score for each scene (v): Computed from the scores of the selected concepts for each scene

viq = wc × vsi

c∈C 'q

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

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

Visual Concept Selection Performance

• MAP

Solr queriesConcepts selection 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.1 0.0892 0.0316 0.0558 0.0842 0.1183 0.168 0.1914 0.1919 0.18980.2 0.1741 0.1366 0.1152 0.1312 0.1503 0.1777 0.1922 0.1919 0.18980.3 0.184 0.1819 0.1806 0.1652 0.1731 0.1848 0.1927 0.1919 0.18980.4 0.1874 0.1883 0.1914 0.1868 0.1889 0.1897 0.1937 0.1919 0.18980.5 0.1875 0.1874 0.1886 0.1928 0.1937 0.1896 0.1939 0.1919 0.18980.6 0.1892 0.1884 0.1886 0.1913 0.1931 0.1946 0.1952 0.1923 0.18980.7 0.1901 0.1901 0.1901 0.191 0.1917 0.1943 0.1948 0.1905 0.18910.8 0.1935 0.1935 0.1935 0.1943 0.1947 0.1959 0.1954 0.1964 0.190.9 0.1946 0.1946 0.1946 0.1952 0.1953 0.1962 0.1961 0.1958 0.1945

• Precision@5

Solr queriesConcepts selection 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.1 0.5533 0.26 0.3133 0.46 0.5467 0.66 0.7 0.7333 0.73330.2 0.72 0.6667 0.5267 0.6267 0.64 0.7 0.7067 0.7333 0.73330.3 0.6867 0.72 0.7067 0.6467 0.7 0.7267 0.7067 0.7333 0.73330.4 0.7 0.7 0.7267 0.6933 0.7133 0.7467 0.7133 0.7333 0.73330.5 0.7133 0.7133 0.7067 0.72 0.74 0.74 0.7133 0.7333 0.73330.6 0.7267 0.7267 0.7267 0.7333 0.7333 0.74 0.7133 0.7333 0.73330.7 0.72 0.72 0.72 0.7267 0.7333 0.7333 0.7133 0.7333 0.73330.8 0.74 0.74 0.74 0.74 0.74 0.7533 0.7467 0.74 0.740.9 0.74 0.74 0.74 0.74 0.74 0.7533 0.7533 0.7533 0.74

• Precision@10 Solr queriesConcepts selection 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.1 0.4033 0.1667 0.2333 0.3233 0.4367 0.55 0.6033 0.6167 0.62670.2 0.5733 0.5 0.43 0.4967 0.51 0.5733 0.6067 0.6167 0.62670.3 0.6033 0.5733 0.5767 0.57 0.5567 0.5967 0.6067 0.6167 0.62670.4 0.59 0.5867 0.6 0.59 0.6 0.6067 0.6067 0.6167 0.62670.5 0.59 0.59 0.5967 0.6 0.59 0.6 0.61 0.6167 0.62670.6 0.61 0.61 0.61 0.61 0.6067 0.5933 0.61 0.6133 0.62670.7 0.61 0.61 0.61 0.61 0.61 0.5967 0.6133 0.6133 0.62330.8 0.6167 0.6167 0.6167 0.62 0.6233 0.6133 0.6233 0.6267 0.62330.9 0.63 0.63 0.63 0.6333 0.6333 0.63 0.6367 0.6367 0.6333

• Precision@20 Solr queriesConcepts selection 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.1 0.2683 0.105 0.17 0.2267 0.3033 0.4017 0.44 0.4483 0.440.2 0.4167 0.345 0.3033 0.3383 0.3933 0.4317 0.44 0.4483 0.440.3 0.435 0.4333 0.4317 0.405 0.4233 0.4417 0.44 0.4483 0.440.4 0.4433 0.4367 0.4433 0.4433 0.4433 0.4433 0.4417 0.4483 0.440.5 0.445 0.4417 0.4417 0.4467 0.4583 0.4483 0.4417 0.4483 0.440.6 0.4467 0.445 0.445 0.45 0.4567 0.4483 0.4417 0.4483 0.440.7 0.4533 0.4533 0.4533 0.455 0.4583 0.4583 0.4417 0.4483 0.43830.8 0.4517 0.4517 0.4517 0.4517 0.4533 0.4517 0.445 0.4483 0.440.9 0.45 0.45 0.45 0.45 0.45 0.4483 0.4483 0.4483 0.4483

Challenge 2e: Combining Visual Concept Selection and Fusion

• Logic (AND/OR) vs Fusion (weighted sum) • Text vs Visual Concepts weight • Visual Concept selection threshold

• 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

0.10.2

0.30.4

0.50.6

0.70.8

0,10,2

0,30,4

0,50,6

0,70,8

Text vs Visual Concept Fusion Weight

Visual Concept Selection Threshold

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

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

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

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.

• The 2013 dataset: 2323 BBC videos of different genres (440 programs)

• 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

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>

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)

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).

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

mGAP results (60s window)

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

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)

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

2013 Hyperlinking Results (P=10 - 60s windows)

The Search and Hyperlinking Demo

Content Analysis

BroadCast Media

Metadata (Subtitles) Lucene/Solr

Media DB

Solr Index

WebService (HTML5/AJAX/PHP)

User Interface

• LinkedTV hyperlinking scenario

Demonstration

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

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)

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.

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.

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

Thank you!

More information: http://www.eurecom.fr/~huet benoit.huet@eurecom.fr

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