Analyzing large multimedia collections in an urban context - Prof. Marcel Worring
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In this talk I will consider the analysis of social media data in an urban context, in particular we look at textual data, visual data and all their metadata to understand social and business phenomena. Analyzing such complex and diverse data poses major challenges for the analyst as the insight of interest is a result of an intricate interplay between the different modalities, their metadata and the evolving knowledge the analyst has about the problem. Our multimedia analytics solutions brings together automatic multimedia analysis and information visualization to give the analyst the optimal opportunities to get insight in complex datasets and use them in applications such as recommending venues to tourists, measuring the effect of city marketing campaigns, or seeing how social multimedia redefines urban borders.
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Analyzing large multimedia collections in an urban context - Prof. Marcel Worring
- 1. 12-7-2016 1 Amsterdam Data Science Marcel Worring Marcel Worring Analyzing large multimedia collections in an urban context Marcel Worring Stevan Rudinac, Jan Zahalka, Dennis Koelma Joost Boonzajer Flaes, Jorrit van den Berg Informatics Institute, Amsterdam Data Science MSc. VU computer Science PhD: UvA Informatics Institute Now: 0.8fte Informatics Institute 0.2fte Amsterdam Business School Associate Director Amsterdam Data Science Amsterdam Data Science Objective and Subjective data Image data Numeric data Geographic data Structured data Unstructured data Temporal data Textual data Open dataOpen Data Geo location .,. Amsterdam, Netherlands Exif .,. Camera: Nikon N60 .,. Focal length: 55 mm .,. Exposure time: 1/200 .,. Flash: off Author .,. josemanuelerre (Flickr) .,. Jose´ Manuel R´ıos Valiente Tags .,. cyclist .,. bike .,. street Comments .,. “I love Amsterdam! great photo!” .,. “Great compostion, beautiful B&W!!” .,. “Estupendo B&N, bella imagen.” . . . Data Sources
- 2. 12-7-2016 2 .,. “Koningsdag, or ‘King’s Day,’ is one of the principal holidays of the Netherlands. . . ” .,. In this case, the image says more than the text Photo: quantz @ Flickr Data Sources Objective and Subjective data Open dataOpen Data + Content Analysis WHAT DOES IS BRING? Professional Recommender Systems Recommender system for tourists 11 Touristic Routing
- 3. 12-7-2016 3 City Sentiment City Marketing Analytics ALGORITHMS Ranking of data Some query defines starting point and order Result Best Worse An image/video/text collection For Social Media • The Ranking can be based on – The objective content of the comments – The subjective content of the comments – The objective visual content – The subjective visual content – ……… • Or any combination of the above Concept detection Learn model Visual examples Positive negative Unknown images Score of presence -> ranking
- 4. 12-7-2016 4 Zebu Requires annotation to learn Animals PeopleLions Lemurs What do we learn? 14,197,122 images, 21841 synsets indexed 1200 trained visual concept detectors for adjective-noun pairs The new trend: Deep learning Krishevsky NIPS 2012 Start with raw pixels, learn all parameters The learned filters Zeiler and Fergus The layered network Krishevsky NIPS 2012 Convolution + pooling + fully connected layers + output layers 60.000.000 parameters to learn But what do all these layers do?
- 5. 12-7-2016 5 Visualizing deep networks Zeiler and Fergus Visualizing deep networks Visualizing deep networks Visualizing deep networks State-of-the-art: GoogleNet and growing …… Makes image search keyword driven Text Analysis D. Blei, 2003 Latent Dirichlet Allocation Latent Dirichlet Allocation
- 6. 12-7-2016 6 Latent Dirichlet Allocation D. Blei, 2003 .,. Generative model, discovers topics and scores them .,. 100 topics are enough to sufficiently cover entire Wikipedia .,. Input: Raw text .,. Output: Topic scores per document 0.054*mexico + 0.049*forest + 0.024*argentina + 0.022*islands + ...+ 0.014*aires Latent Dirichlet Allocation We treat comments or sets tags as documents VENUE RECOMMENDER .,. Venue recommendation — suggesting places of interest (venues) based on user preferences .,. The classic approach is collaborative filtering utilizing the user-item matrix The task .,. City Melange — a venue explorer utilizing multimedia analytics techniques .,. Content-based — based solely on the content of venue-related social media .,. Multimodal — combining content from images and the associated text .,. Interactive — user preferences are modelled on the fly as you explore the city .,. Cross Platform — integrates data from diverse social platforms City Melange Characteristics Venue information Venue images Images, metadata User data Q(venue name,geo) Data Gathering
- 7. 12-7-2016 7 Content V Images T Tags Comments . . . VC Venues Users U Data Analysis Content V Images T Tags Comments . . . VC Venues Users U Features VF ConvNet TF LDA Data Analysis Content V Images T Tags Comments . . . VC Venues Users U Features VF ConvNet TF LDA Clustering Processed data VT V Visual venue topics Data Analysis Content V Images T Tags Comments . . . VC Venues Users U Features VF ConvNet TF LDA Clustering Processed data VT V Visual venue topics Visual user topicsVT U Data Analysis Content V Images T Tags Comments . . . VC Venues Users U Features VF ConvNet TF LDA Clustering Processed data VT V VT U Visual venue topics Visual user topics Text venue topicsT V T Data Analysis Content V Images T Tags Comments . . . VC Venues Users U Features VF ConvNet TF LDA Clustering Processed data VT V VT U T V T Visual venue topics Visual user topics Text venue topics Text user topicsT U T Data Analysis
- 8. 12-7-2016 8 Content V Images T Tags Comments . . . VC Venues Users U Features VF ConvNet TF LDA Clustering Processed data VT V T T U T Visual venue topics Visual user topics Text venue topics Text user topics User-venue matrix VT U V T UV Data Analysis .,. ACM Multimedia Grand Challenge 2014 1st Prize .,. newyorkermelange.com VT ,TT V V Venue topics VT ,TT U U User topics Users U UV User-venue matrix Interactive Recommendation VT ,TT V V Venue topics VT ,TT U U User topics Users U UV User-venue matrix Grid Interactive Recommendation VT ,TT V V Venue topics VT ,TT U U User topics Users U UV User-venue matrix Grid Rel. venues VT ,TT + + Positives Interactive Recommendation VT ,TT V V Venue topics VT ,TT U U User topics Users U UV User-venue matrix Grid Rel. venues VT ,TT + + Positives User ranking Interactive Recommendation
- 9. 12-7-2016 9 VT ,TT V V Venue topics VT ,TT U U User topics Users U UV User-venue matrix Grid Rel. venues VT ,TT + + Positives User ranking V− ,T − T T Negatives Rand. sample Interactive Recommendation VT ,TT V V Venue topics VT ,TT U U User topics Users U UV User-venue matrix Grid Rel. venues VT ,TT + + Positives User ranking V− ,T − T T Negatives Rand. sample Linear USSVM User ranking Suggested users Interactive Recommendation VT ,TT V V Venue topics VT ,TT U U User topics Users U UV User-venue matrix Grid Rel. venues VT ,TT + + Positives User ranking V− ,T − T T Negatives Rand. sample Linear USSVM User ranking Suggested users Venue ranking Interactive Recommendation VT ,TT V V Venue topics VT ,TT U U User topics Users U UV User-venue matrix Grid Rel. venues VT ,TT + + Positives User ranking V− ,T − T T Negatives Rand. sample Linear USSVM User ranking Suggested users Venue ranking Venue ranking VS Suggested venues Interactive Recommendation VT ,TT V V Venue topics VT ,TT U U User topics Users U UV User-venue matrix Grid Rel. venues VT ,TT + + Positives User ranking V− ,T − T T Negatives Rand. sample SVM User ranking Linear US Suggested users Venue ranking Venue ranking VS Suggested venues (US,VS) Suggestions Interactive Recommendation VT ,TT V V Venue topics VT ,TT U U User topics Users U UV User-venue matrix Grid Rel. venues VT ,TT + + Positives User ranking V− ,T − T T Negatives Rand. sample SVM User ranking Linear US Suggested users Venue ranking Venue ranking VS Suggested venues (US,VS) Suggestions Map Interactive Recommendation
- 10. 12-7-2016 10 VT ,TT V V Venue topics VT ,TT U U User topics Users U UV User-venue matrix Grid Rel. venues VT ,TT + + Positives User ranking V− ,T − T T Negatives Rand. sample Linear USSVM User ranking Suggested users Venue ranking Venue ranking VS Suggested venues (US,VS) Suggestions Map Relevance indication Interactive Recommendation Recommender system for tourists 56 1. Can we recommend the right type of venue? 2. Can we recommend mainstream venues to mainstream tourists and specialized venues to afficionados? Evaluation .,. 621 fine-grained venue types (Japanese restaurant, skate park. . . ) .,. 100 artificial actors, use 75% of the data to seed Melange .,. Perform 10 interaction rounds Evaluation • .,. City Melange • .., Visual modality only • .., Text modality only • .., Multimedia (vis + txt) • .,. Recommender baselines • .., WRMF — Weighted regularized matrix factorization • .., BPRMF — Bayesian personalized ranking matrix factorization • .,. Popularity ranking (PopRank) — most visited venues according to Foursquare Methods Compared .,. New York — 1.07M images and associated text from Foursquare, Flickr, and Picasa .,. Amsterdam — 56K images and associated text from Foursquare and Flickr Data Collection
- 11. 12-7-2016 11 1 2 poprank melange_vis 3 4 5 6 Interaction round 7 8 9 10 0.0 0.6 0.5 0.4 0.3 0.2 0.1 Venuetype precision bprmf melange_txt wrmf melange_mm New York 1 2 poprank melange_vis 3 4 5 6 Interaction round 7 8 9 10 0.0 0.2 0.4 0.6 0.8 1.0 Venuetype recall bprmf melange_txt wrmf melange_mm New York 1 2 poprank melange_vis 3 4 5 6 Interaction round 7 8 9 10 0.0 0.6 0.5 0.4 0.3 0.2 0.1 Venuetype precision bprmf melange_txt wrmf melange_mm Amsterdam 1 2 poprank melange_vis 3 4 5 6 Interaction round 7 8 9 10 0.0 0.2 0.4 0.6 0.8 1.0 Venuetype recall bprmf melange_txt wrmf melange_mm Amsterdam 0.0 0.2 0.4 0.6 0.8 1.0 Trueuser-venuedistribution density 0.2 0.1 0.0 0.1 melange 0.2 0.3 Density difference mm wrmf bprmf poprank Distribution of recommendations TOURIST ROUTING
- 12. 12-7-2016 12 SceneMash • Data collection 150,000 geotagged Flickr and Foursquare images from the region of Amsterdam Metadata associated with the images: - image title - description - tags - geotags SceneMash SceneMash SceneMash Demo CITY SENTIMENT Data Collection 64K GeoTagged Tweets with Images Various neighborhood statistics (17 variables) 64K GeoTagged Images and comments Amsterdam Neighborhoods
- 13. 12-7-2016 13 Methodology Sentiment Maps Sentimentanalysis Sentiment Maps Sentimentanalysis Finding correlations textual and visual content textual and visual content various statistics Sentimentanalysis Correlation Analysis Correlations Flickr Twitter Correlations are only found with multimodal sentiment Redefined Neighborhoods People with similar social media interests
- 14. 12-7-2016 14 MARKETING ANALYTICS WHAT WE HAVE “The purpose of computing is insight, not numbers.” Richard Hamming 1962 So what we want? Insight What is insight? Insight Complex Insight is complex, involving all or large amounts of the given data in a synergistic way, not simply individual data values. Deep Insight builds up over time, accumulating and building on itself to create depth often generating further questions and, hence, further insight. Qualitative Insight is not exact, can be uncertain and subjective, and can have multiple levels of resolution. Unexpected Insight is often unpredictable, serendipitous, and creative. Relevant Insight is deeply embedded in the data domain, connecting the data to existing domain knowledge and giving it relevant meaning going beyond dry data analysis, to relevant domain impact. North CG&A, 2006 “Computers are incredibly fast, accurate, and stupid. Humans are incredibly slow, inaccurate and brilliant. The marriage of the two is beyond imagination” Leo Cherne 1968
- 15. 12-7-2016 15 Visual Analytics • Combine the power of computer and human • Compute power • Storage capacity • Flexibility • Creativity • Expert knowledge Definition Multimedia Analytics = Multimedia Analysis + Visual Analytics Ref:Chinchor2010 Multimedia Analytics INSIGHT Analytics • What is the best known Analytic tool? Yes the Spreadsheet Analytics Fischer et.al, TVCG 2010. MediaTable Columns denote concept scores can be used for sorting Colors denote categories and buckets are used to collect elements of (sub-) category Heatmap like visualization Grey values denote values between 0 and 1 Allows to see correlations Filters/sort order can be specified Refs: deRooij2010b, deRooij2013
- 16. 12-7-2016 16 Multimedia Pivot Tables ROWVARIABLE:Decompose FILTER VARIABLES: Define active data set Concepts Tags Nominals COLUMN AGGREGATION Integers COLUMN VARIABLES: Sort and Weight VALUE VALUE VALUE VALUE ROWAGGREGATION Visualizations Type Filter Column Row Value Visualization Images Selection to bucket x Individual images Sorted list of images Nominal Label selection x Individual labels Sorted and weighted text histogram Buckets Bucket selection x Individual buckets Weighted histogram Geo Selection to bucket x x Map with weighted elements Numeric Range selection Weights 7-point summary Sum, max, avg, weighted distribution Concepts Range selection Weights 7-point summary Weighted distribution Tags Tag selection Weights Individual tags Sorted and weighted tag histogram Statistics driven decomposition Column aggregation Row aggregation Top-N ConceptsRow specific concepts Concept based sorting Relevance based sorting
- 17. 12-7-2016 17 BM-25 BASED RANKING Demo https://staff.fnwi.uva.nl/m.worring/pivot-tables.html Learning from interaction Employing user interaction pos neg Selection of pos/neg examples Some elements in the collection are labeled Many are not
- 18. 12-7-2016 18 Employing user interaction User Pool-Query Set Labeled Resultant set Learning Algorithm Interactive Learning Strategy Active Learning Chen in 2005 was the first to explore this for Video Retrieval Relevance feedback Ref: Huang2008 Relevance feedback Try to find boundary in feature space best separating positive from negative examples F F1 F2 Measure of class membership probability Relevance feedback In the next iteration I will have more samples hence a better estimate of the boundary F F1 F2 This process is usually known as relevance feedback Active Learning In active learning the system decides which elements to show for feedback and which not. F F1 F2 For the system it is relevant to know this label The system can safely assume this sample is also negative Automatic AND interactive SVM based relevance feedback Interactive categorization Three interactive strategies • Fully interactive – User is interactively performing the sort/select/categorize process • Manual relevance feedback – In addition to the above the user can perform relevance feedback on any of the categories • Unobtrusive relevance feedback – In addition to the above the system automatically indicates new potentially relevant elements
- 19. 12-7-2016 19 Fully interactive On demand suggestions After categorizing some elements Learn and apply model for user selected bucket Uncategorized images Category suggestions Unobtrusive assistance Continously observe what happens Learn and apply model for system selected bucket Uncategorized images Category suggestions Results: elements found • significant at the p=0.01 level compared to baseline o significant at the p=0.01 level compared to manual Task 1: specific, high visual similarity Task 2: generic visually diverse, concept available Task 3: generic visually diverse, concept available Task 4: generic visually diverse, no concept available SCALABILITY
- 20. 12-7-2016 20 [Zahálka and Worring, VAST 2014] B.P. Jonsson et.al. MMM 2016 WRAP-UP Objective and Subjective data Image data Numeric data Geographic data Structured data Unstructured data Temporal data Textual data Open dataOpen Data The applications The Algorithms And its variations