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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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IEEE Transactions on Pattern Analysis and Machine Intelligence
LogoSeeker: a system for detecting and matching logos in natural images
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ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
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ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
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Brand Associations, one of central concepts in marketing, describe customers' top-of-mind attitudes or feelings toward a brand. Thus, this consumer-driven brand equity often attains the grounds for purchasing products or services of the brand. Traditionally, brand associations are measured by analyzing the text data from consumers' responses to the survey or their online conversation logs. In this paper, we propose to go beyond text data and leverage large-scale online photo collections contributed by the general public, which have not been explored so far. As a first technical step toward the study of photo-based brand associations, we aim to jointly achieve the following two visualization tasks in a mutually-rewarding way: (i) detecting and visualizing core visual concepts associated with brands, and (ii) localizing the regions of brand in the images. With experiments on about five millions of images of 48 brands crawled from five popular online photo sharing sites, we demonstrate that our approach can discover complementary views on the brand associations that are hardly mined from the text data. We also quantitatively show that our approach outperforms other candidate methods on the both visualization tasks.