MULTIMEDIA ANALYTICS: SYNERGY BETWEEN HUMAN AND MACHINE BY VISUALIZATION
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1 MULTIMEDIA ANALYTICS: SYNERGY BETWEEN HUMAN AND MACHINE BY VISUALIZATION Marcel Worring, Jan Zahálka, Stevan Rudinac Intelligent Systems Lab Amsterdam Amsterdam Data Science University of Amsterdam Amsterdam Data Science
2 INTRODUCTION Multimedia data increasingly important
3 INTRODUCTION Multimedia data increasingly important Valuable sources of knowledge, for example: Forensics: analyze multimedia data for evidence of ISIS involvement Travel industry: analyze social media data to map trending places of interest...
4 MULTIMEDIA AS A KNOWLEDGE SOURCE Night Watch by Rembrandt. How to describe it? g
5 MULTIMEDIA AS A KNOWLEDGE SOURCE Art? Painting? People? Military unit? Amsterdam? g
6 MULTIMEDIA AS A KNOWLEDGE SOURCE Art? Painting? People? Military unit? Amsterdam?... Content, technical parameters, geo location,...
7 MULTIMEDIA AS A KNOWLEDGE SOURCE Description depends on context provided by the analyst Analyst needs to interact with the system
8 MULTIMEDIA AS A KNOWLEDGE SOURCE Image Tags Comments Metadata... Multimedia items contain multiple types of data Integrating them improves the information gain
9 M ULTIMEDIA AS A K NOWLEDGE S OURCE I What if we have millions of images, tags, metadata...? Intelligent navigation capabilities required from the system
10 MULTIMEDIA ANALYTICS How do we move towards interactive, intelligent, and integrated multimedia systems?
11 MULTIMEDIA ANALYTICS How do we move towards interactive, intelligent, and integrated multimedia systems? Possible answer: multimedia analytics InfoVis Multimedia Analytics Visual Analytics Multimedia Analysis
12 RELATED WORK Extensive survey work involving 800 references
13 RELATED WORK Extensive survey work involving 800 references Covered relevant work from last 10 years: Multimedia analytics Multimedia visualization Information visualization Visual analytics Automated multimedia analysis
14 RELATED WORK Extensive survey work involving 800 references Covered relevant work from last 10 years: Multimedia analytics Multimedia visualization Information visualization Visual analytics Automated multimedia analysis Multimedia Analytics Article Library (MAAL): staff.fnwi.uva.nl/j.zahalka/maal.html 374 catalogued references
15 PIPELINE Visualization Data MM collection Images Annotations Metadata interactive navigation model update directions Model Knowledge Category 1 people 61 items... Category 2 nature 93 items Multimedia instantiation of the visual analytics process (Keim et al., Visual analytics: Scope and challenges, 2008)
16 TASK MODEL Exploration Search Exploration: uncovering the overall structure Search: finding particular items
17 TASK MODEL Exploration Search Exploration: uncovering the overall structure Search: finding particular items Exploration-search axis: E-S ratio changes dynamically
18 TASK MODEL Exploration Start Search Exploration: uncovering the overall structure Search: finding particular items Exploration-search axis: E-S ratio changes dynamically
19 TASK MODEL Exploration Start End Search Exploration: uncovering the overall structure Search: finding particular items Exploration-search axis: E-S ratio changes dynamically
20 TASK MODEL Exploration Start End Search Exploration: uncovering the overall structure Search: finding particular items Exploration-search axis: E-S ratio changes dynamically Mental model attributes: semantic categorical
21 TASK MODEL Exploration Start Categorization End Search Exploration: uncovering the overall structure Search: finding particular items Exploration-search axis: E-S ratio changes dynamically Mental model attributes: semantic categorical
22 CATEGORIZATION Categorization assigning individual multimedia items into categories defined by the analyst
23 CHALLENGE: THE GAPS Multimedia analysis capabilities very different for humans and machines
24 CHALLENGE: THE GAPS semantic gap Complex and abstract semantics Recognized instantly Put in context Limited semantics Takes time, computationally costly No context Multimedia analysis capabilities very different for humans and machines Semantic gap [Smeulders et al. 2000] richness of semantics
25 CHALLENGE: THE GAPS pragmatic gap New categories on the fly Non-exclusive categories Dynamic category semantics Static no. of classes Exclusive classes Static class semantics Multimedia analysis capabilities very different for humans and machines Semantic gap [Smeulders et al. 2000] richness of semantics Pragmatic gap (our work) flexibility of the model
26 SIMILARITY BROWSER
27 FORK BROWSER
28 PHOTO CUBE
29 MULTIMEDIA PIVOT TABLES
30 STATE OF THE ART pragmatic gap Advanced Goal Intermediate Similarity browser MediaTable INA browser Limited Canopy Informedia svisit I-SI Newdle semantic gap Limited Intermediate Advanced
31 STATE OF THE ART pragmatic gap Advanced Goal Intermediate Similarity browser MediaTable INA browser Limited Canopy Informedia svisit I-SI Newdle semantic gap Limited Intermediate Advanced Systems advance w.r.t. gaps
32 STATE OF THE ART pragmatic gap Advanced Goal Intermediate Similarity browser MediaTable INA browser Limited Canopy Informedia svisit I-SI Newdle semantic gap Limited Intermediate Advanced Systems advance w.r.t. gaps Algorithms and techniques allow realization of our model
33 INSTANTIATING THE MODEL
34 NEW YORKER MELANGE Interactive New York venue recommender
35 NEW YORKER MELANGE Interactive New York venue recommender Explore the city through the eyes of social media users that share interests with you.
36 NEW YORKER MELANGE Interactive New York venue recommender Explore the city through the eyes of social media users that share interests with you. newyorkermelange.com
37 NEW YORKER MELANGE Interactive New York venue recommender Explore the city through the eyes of social media users that share interests with you. newyorkermelange.com ACM Multimedia Grand Challenge st Prize
38 NEW YORKER MELANGE: INGREDIENTS Grid, map Visual & text features for venues & users indicate relevant users & venues suggest more relevant users & venues Interesting venues to visit SVM
39 NEW YORKER MELANGE: INGREDIENTS Grid, map Visual & text features for venues & users indicate relevant users & venues suggest more relevant users & venues Interesting venues to visit SVM Exploration NY Melange Search
40 N EW YORKER M ELANGE
41 N EW YORKER M ELANGE
42 DATASET New York venues Venue images
43 DATASET Q(venue name,geo) New York venues Venue images Images, metadata
44 DATASET >1M New York venue images with metadata
45 DATASET >1M New York venue images with metadata Real dataset with a purpose
46 DATASET >1M New York venue images with metadata Real dataset with a purpose Query strategy designed to reduce noise Exploitable size-noise tradeoff
47 DATASET >1M New York venue images with metadata Real dataset with a purpose Query strategy designed to reduce noise Exploitable size-noise tradeoff Each image has a venue category label ready for classification
48 VENUE/USER TOPICS Dataset Images Foursquare Flickr Picasa Annotations
49 VENUE/USER TOPICS Dataset Images Foursquare Flickr Picasa Annotations ConvNet LDA Features 1000 visual concepts 100 latent topics
50 VENUE/USER TOPICS Dataset Images Foursquare Flickr Picasa Annotations ConvNet LDA Features 1000 visual concepts 100 latent topics Clustering Venue topics Visual Text User topics Visual Text
51 USER PREFERENCE LEARNING Initial interface
52 USER PREFERENCE LEARNING empty Negatives Initial interface +relevant venues Positives
53 USER PREFERENCE LEARNING empty Negatives (random sample) User topics Initial interface +relevant venues Positives
54 USER PREFERENCE LEARNING empty Negatives (random sample) User topics Initial interface Linear SVM +relevant venues Positives
55 USER PREFERENCE LEARNING empty Negatives (random sample) User topics Initial interface Linear SVM User ranking +relevant venues Positives
56 USER PREFERENCE LEARNING empty Negatives (random sample) User topics Initial interface Linear SVM User ranking +relevant venues Positives Venue topics Venue selection
57 USER PREFERENCE LEARNING empty Negatives (random sample) User topics Initial interface Linear SVM User ranking +relevant venues Positives Map Venue topics Venue interface selection
58 USER PREFERENCE LEARNING empty Negatives (random sample) +non-relevant User topics Initial interface Linear SVM users User ranking +relevant venues Positives Map +relevant users Venue topics Venue interface selection
59 USER PREFERENCE LEARNING empty Negatives (random sample) +non-relevant User topics Initial interface Linear SVM users User ranking +relevant venues Positives Map +relevant users Venue topics Venue interface selection
60 EVALUATION: SCHEME Real user data
61 EVALUATION: SCHEME Real user data 25% of the visited venues withheld, rest used to seed the system
62 EVALUATION: SCHEME Real user data 25% of the visited venues withheld, rest used to seed the system 10 interaction rounds
63 EVALUATION: SCHEME Real user data 25% of the visited venues withheld, rest used to seed the system 10 interaction rounds Measure: average recall of the withheld venues
64 EVALUATION: SCHEME Real user data 25% of the visited venues withheld, rest used to seed the system 10 interaction rounds Measure: average recall of the withheld venues Only exact withheld venues count as match
65 EVALUATION: RESULTS Baseline NYM-V NYM-T NYM-VT 0.25 Average Recall Interaction Round
66 FUTURE OF MELANGE: SOFTWARE Consolidated Melange deployable everywhere
67 FUTURE OF MELANGE: SOFTWARE Consolidated Melange deployable everywhere Amsterdam
68 FUTURE OF MELANGE: SOFTWARE Consolidated Melange deployable everywhere Amsterdam Hong Kong
69 FUTURE OF MELANGE: SOFTWARE Consolidated Melange deployable everywhere Amsterdam Hong Kong Beijing
70 FUTURE OF MELANGE: SOFTWARE Consolidated Melange deployable everywhere Amsterdam Hong Kong Beijing Washington, D. C.
71 FUTURE OF MELANGE: SOFTWARE Consolidated Melange deployable everywhere Amsterdam Hong Kong Beijing Washington, D. C. Prague
72 FUTURE OF MELANGE: SOFTWARE Consolidated Melange deployable everywhere Amsterdam Hong Kong Beijing Washington, D. C. Prague Rennes
73 FUTURE OF MELANGE: SOFTWARE Consolidated Melange deployable everywhere Amsterdam Hong Kong Beijing Washington, D. C. Prague Rennes...
74 C ONCLUSION I A model of multimedia analytics integration, tasks and challenges Images Text Metadata
75 C ONCLUSION I I A model of multimedia analytics integration, tasks and challenges Based on extensive survey work I Multimedia Analytics Article Library: staff.fnwi.uva.nl/j.zahalka/maal.html Images Text Metadata
76 C ONCLUSION I I A model of multimedia analytics integration, tasks and challenges Based on extensive survey work I I Multimedia Analytics Article Library: staff.fnwi.uva.nl/j.zahalka/maal.html Current state-of-the-art techniques allow realization I Ample research opportunities in closing the gaps Images Text Metadata
77 C ONCLUSION I I A model of multimedia analytics integration, tasks and challenges Based on extensive survey work I I Current state-of-the-art techniques allow realization I I Multimedia Analytics Article Library: staff.fnwi.uva.nl/j.zahalka/maal.html Ample research opportunities in closing the gaps Model already successfuly instantiated I New Yorker Melange: newyorkermelange.com Images Text Metadata
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