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Discovering Pictorial Brand Associations from Large-Scale Online Image Data Gunhee Kim* Eric P. Xing 1 School of Computer Science, Carnegie Mellon University.

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Presentation on theme: "Discovering Pictorial Brand Associations from Large-Scale Online Image Data Gunhee Kim* Eric P. Xing 1 School of Computer Science, Carnegie Mellon University."— Presentation transcript:

1 Discovering Pictorial Brand Associations from Large-Scale Online Image Data Gunhee Kim* Eric P. Xing 1 School of Computer Science, Carnegie Mellon University *: (now with Disney Research Pittsburgh) December 7, 2013

2 Problem Statement Dataset Algorithm Visualization – Brand Association Maps Results Conclusion Outline 2

3 3 A set of values or assets linked to a brand's name and symbol Brand Equity Bearing competitive advantage over its competitors Boosting efficiency and effectiveness of marketing programs Attaining price premium due to increased customer satisfaction and loyalty Interbrand (2013)

4 4 [Keller 1993] [Aaker 1991] Brand Knowledge Brand AwarenessBrand Image Brand recall Brand Recognition Types of brand association Favorability of brand association Strength of brand association Uniqueness of brand association Brand Equity Name awareness Brand loyalty Perceived quality Brand association Other properties (ex. trademarks, patens) A set of values or assets linked to a brand's name and symbol Brand Equity Bearing competitive advantage over its competitors Boosting efficiency and effectiveness of marketing programs Attaining price premium due to increased customer satisfaction and loyalty

5 5 [Keller 1993] [Aaker 1991] Brand Knowledge Brand AwarenessBrand Image Brand recall Brand Recognition Types of brand association Favorability of brand association Strength of brand association Uniqueness of brand association Brand Equity Name awareness Brand loyalty Perceived quality Brand association Other properties (ex. trademarks, patens) A set of values or assets linked to a brand's name and symbol Brand Equity Bearing competitive advantage over its competitors Boosting efficiency and effectiveness of marketing programs Attaining price premium due to increased customer satisfaction and loyalty

6 6 Brand Associations (What Comes to Mind When You Think of …) soccer, national teams, … basketball, golf, … swimming, diving, beach, … A set of associations that consumers perceive with the brand Top-of-mind attitudes or feelings toward the brand

7 7 Brand Associations (What Comes to Mind When You Think of …) A set of associations that consumers perceive with the brand Top-of-mind attitudes or feelings toward the brand Consumer-driven brand equity I need a mens golf shirts… What would I buy? I associatewith How can we find out peoples brand associations?

8 8 A Traditional Way to Brand Associations Collect direct consumer responses to carefully-designed questionnaires In-N-Out [Dane et al. 2010] Reeses [Till et al. 2011] Expensive and time-consuming Sampling bias /common methods bias Can Web data save us? Cheap and immediate Candid impression from a large crowd of potential customers

9 9 Built from consumer-generated text data on online social media (eg. Weblogs, Boards, Wiki) s Brand Association Map The most important concepts and themes that consumers have

10 10 Our Idea: Photo-based Brand Associations Take advantage of large-scale online photo collections No previous attempts so far to leverage the pictures Our underlying rational Take a picture and tag as burger+king Pictorial opinion on burger+king Crawl millions of online images tagged as burger+king We can read peoples candid pictorial opinions on burger+king Pictures cannot replace texts. They are complementary. ex) Nike shoes are too expensive. Their design is tacky!

11 11 2. Take or bookmark pictures of the products you wish to buy. 1. Brands are recognized by images. Why are online images helpful to discover brand associations? Our Idea: Photo-based Brand Associations Take advantage of large-scale online photo collections No previous attempts so far to leverage the pictures

12 12 Why are online images helpful to discover brand associations? Our Idea: Photo-based Brand Associations Take advantage of large-scale online photo collections No previous attempts so far to leverage the pictures 3. Capture interactions between users and products in natural social context

13 13 Identifying a small number of exemplars / image clusters Project the clusters into a circular layout Objective of This Paper 1. (Image-level) Detect and visualize key pictorial concepts associated with brands Two visualization tasks as a first technical step

14 14 A basic function to reveal interactions between users and products Potential benefits: content-based retrieval and online multimedia ad Objective of This Paper 2. (Subimage-level) Localize the regions of brand in images Two visualization tasks as a first technical step

15 Problem Statement Dataset Algorithm Visualization – Brand Association Maps Results Conclusion Outline 15

16 16 Image Dataset from Five Websites 4,720,724 images of 48 brands of four categories Luxury Sports Beer Fastfood Two largest and most popular photo sharing Web communities Pictures bookmarked by users Various forms of artwork by users Photo hosted for twitter

17 Problem Statement Dataset Algorithm Visualization – Brand Association Maps Results Conclusion Outline 17

18 18 Approach – KNN Graph Generation Input: a set of images of a brand (e.g. louis+vuitton): Feature extraction Dense feature extraction of Color SIFT and HOG Histogram intersection for similarity measure KNN Similarity graph Approximate method (Multiple random divide-and-conquer using meta-data) [Wang et al. 2012] in O(NlogN)

19 19 Approach – Exemplar Detection/Clustering Detecting L number of exemplars A small set of representative images Diversity ranking algorithm (temperature maximization) [Kim & Xing 2011] Solving submodular optimization on

20 20 Approach – Exemplar Detection/Clustering Each image I is associated with its closest exemplar Random walk model (What exemplar is most likely reachable from each I ?) Clustering

21 21 Approach – Brand Localization via Cosegmentation Find the regions that are most relevant to the brand Use MFC algorithm [Kim&Xing. 2012] to each cluster of coherent images Make parallelization easy and prevent cosegmenting images of no commonality Optionally, generate bounding boxes

22 22 Approach – Closing a Loop Go back to graph generation (Our argument) even imperfect segmentation helps obtain better image similarity measurement. Not robust against the location and scale change Image similarity as the mean of best matched segment similarity

23 Problem Statement Dataset Algorithm Visualization – Brand Association Maps Results Conclusion Outline 23

24 24 Embedding on a Circular Layout Goal: Compute two coordinates of key clusters Compute stationary distribution of nodes Radial distance Radial distance: a larger cluster closer to the center Angular distance: the smaller, the higher correlation Radial distances Angular distance Sum of stationary distributions of nodes of each cluster the probability that a random walker stays (Based on Nielsons BAM)

25 25 Embedding on a Circular Layout Angular distance Radial distances Angular distance Using spherical Laplacian eigenmap [Carter et al. 2009.] Pairwise similarity btw clusters S using the random walk with restart [Sun et al. 2005] Similar clusters should have smaller angle difference Penalty to avoid a trivial solution Goal: Compute two coordinates of key clusters Radial distance: a larger cluster closer to the center Angular distance: the smaller, the higher correlation (Based on Nielsons BAM)

26 Problem Statement Dataset Algorithm Visualization – Brand Association Maps Results Examples of Brand Association Maps Quantitative results for exemplar detection/clustering Quantitative results for brand localization Correlations btw Web image data and actual sales data Conclusion Outline 26

27 27 Experiments – Brand Association Maps Brands' characteristic visual themes Events sponsored by brands Weddings

28 28 Sub-M: Multiple runs of our clustering + cosegmentation Sub: Our clustering without cosegmentation Kmean/Spect: K-mean clustering / Spectral clustering LP: Label propagation [Raghavan et al. 2007] AP: Affinity propagation [Frey & Dueck 2008] Experiments – Exemplar Detection/Clustering Groundtruth for clustering accuracy Randomly select 100 sets of three ( i,j,k ) images per class Accuracy is measured by how many sets are correctly clustered Manually select which of ( j,k ) is closer to i : Wilcoxon-Mann- Whitney statistics Similarity btw the clusters of image i and j 100

29 29 Experiments – Brand Localization Task: Foreground detection MFC-M: Multiple runs of our clustering + cosegmentation MFC-S: Single run of our clustering + cosegmentation MFC: Random partition + our cosegmentation COS: Submodular optimization [Kim et al. ICCV11] LDA: LDA-based localization [Russell et al. CVPR06] Manually annotate 50 images per class Accuracy is measured by intersection-over-union

30 30 Experiments – Brand Localization Task: Foreground detection Manually annotate 50 images per class Accuracy is measured by intersection-over-union Examples

31 31 Experiments – Correlation with Sales Data Photo volumes vs. Market share Nikes market share is 57.6% in sports brands. Hows about image volumes? Based on brands annual reports Observations Ranking are roughly similar, but the proportions do not agree. Guinness : premium beer (more photo data) Taco+bell : cheap fastfood brand (fewer photo data)

32 Problem Statement Dataset Algorithm Visualization – Brand Association Maps Results Conclusion Outline 32

33 33 Visualizing core pictorial concepts associated with brands Study of brand associations from millions of Web images Experiments on 5 Millions images of 48 brands from five popular websites Conclusion Complementary views on the brand associations beyond texts Jointly achieving two levels of visualization tasks Localizing the regions of brand in images Various potential applications Online multimedia contextual advertisement, competitor mining

34 34 Press Release Thank You! Extended version will be presented in http://www.cs.cmu.edu/news/carnegie-mellon-researchers-create- brand-associations-mining-millions-images-social-media-sites


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