Abstract
Product visualization is able to help users easily get knowledge about the visual appearance of a product. It is useful in many application and commercialization scenarios. However, the existing product image search on e-commerce Web sites or general search engines usually get insufficient search results or return images that are redundant and not relevant enough. In this article, we present a novel product visualization approach that automatically collects a set of diverse and relevant product images by exploring multiple Web sources. Our approach simultaneously leverages Amazon and Google image search engines, which represent domain-specific knowledge resource and general Web information collection, respectively. We propose a conditional clustering approach that is formulated as an affinity propagation problem regarding the Amazon examples as information prior. The ranking information of Google image search results is also explored. In this way, a set of exemplars can be found from the Google search results and they are provided together with the Amazon example images for product visualization. Experiments demonstrate the feasibility and effectiveness of our approach.
- Benitez, A., Beigi, M., Beigi, I., and Chang, S. F. 1998. A content-based image meta-search engine using relevance feedback. IEEE Internet Comput. 2, 4, 59--69. Google Scholar
Digital Library
- Chen, Y., Yu, N., Luo, B., and Chen, X. W. 2010. Ilike: Integrating visual and textual features for vertical search. In Proceedings of the ACM International Conference on Multimedia. Google Scholar
Digital Library
- Deselaers, T., Gass, T., Dreuw, P., and Ney, H. 2009. Jointly optimising relevance and diversity in image retrieval. In Proceeding of the ACM International Conference on Image and Video Retrieval (CIVR’09). 39:1--39:8. Google Scholar
Digital Library
- Dueck, D. and Frey, B. J. 2007. Non-metric affinity propagation for unsupervised image categorization. In Proceedings of the IEEE International Conference on Computer Vision.Google Scholar
- Frey, B. J. and Dueck, D. 2007. Clustering by passing messages between data points. Sci. 315, 5814, 972--976.Google Scholar
- Givoni, I. E. and Frey, B. J. 2009. A binary variable model for affinity propagation. Neural Comput. 21, 6, 1589--1600. Google Scholar
Digital Library
- He, J., Lin, T.-H., Feng, J., and Chang, S.-F. 2011. Mobile product search with bag of hash bits. In Proceedings of the ACM International Conference on Multimedia. Google Scholar
Digital Library
- Hurley, N. and Zhang, M. 2011. Novelty and diversity in top-n recommendation - Analysis and evaluation. ACM Trans. Internet Technol. 10, 4. Google Scholar
Digital Library
- Jia, Y., Wang, J., Zhang, C., and Hua, X. S. 2008. Finding image exemplars using fast sparse affinity propagation. In Proceeding of the 16th ACM International Conference on Multimedia (MM’08). 639--642. Google Scholar
Digital Library
- Jing, F., Wang, C., Yao, Y., Deng, K., Zhang, L., and Ma, W. Y. 2006. Igroup: Web image search results clustering. In Proceedings of the ACM International Conference on Multimedia. Google Scholar
Digital Library
- Jing, Y. and Baluja, S. 2008. Pagerank for product image search. In Proceeding of the International World Wide Web Conference. Google Scholar
Digital Library
- Kennedy, L. and Chang, S. F. 2008. Query-adaptive fusion for multimodal search. Proc. IEEE 96, 4, 567--588.Google Scholar
- Lew, M. S., Sebe, N., Djeraba, C., and Jain, R. 2006. Content-based multimedia information retrieval: State of the art and challenges. ACM Trans. Multimedia Comput. Comm. Appl. 2, 1, 1--19. Google Scholar
Digital Library
- Li, J., Allinson, N., Tao, D., and Li, X. 2006. Multi-training support vector machine for image retrieval. IEEE Trans. Image Process. 15, 11, 3597--3601. Google Scholar
Digital Library
- Liu, D., Wang, M., Hua, X. S., and Zhang, H. J. 2011. Semi-automatic tagging of photo albums via exemplar selection and tag inference. IEEE Trans. Multimedia 13, 1, 82--91. Google Scholar
Digital Library
- Liu, R., Yang, L., and Hua, X. S. 2009. Image search result summarization with informative priors. In Proceeding of the 9th Asian Conference on Computer Vision (ACCV’09). 485--495. Google Scholar
Digital Library
- Lowe, D. 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 2, 91--110. Google Scholar
Digital Library
- Minium, E. W., King, B. M., and Bear, G. 1970. Statistical Reasoning in Psychology and Education. Wiley, New York.Google Scholar
- Nister, D. and Stewenius, H. 2006. Scalable recognition with a vocabulary tree. In Proceedngs of the IEEE International Conference on Computer Vision and Pattern Recognition. Google Scholar
Digital Library
- Pang, H., Shen, J., and Krishnan, R. 2009. Privacy-preserving, similarity-based text retrieval. ACM Trans. Internet Technol. 10, 1. Google Scholar
Digital Library
- Rother, C., Kumar, S., Kolmogorov, V., and Blake, A. 2005. Digital tapestry. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. Google Scholar
Digital Library
- Tsai, S. S., Chen, D., Chandrasekhar, V., Takacs, G., Cheung, N. M., et al. 2010. Mobile product recognition. In Proceedings of the ACM International Conference on Multimedia. Google Scholar
Digital Library
- Smeulders, A. W. M., Member, S., Worring, M., Santini, S., Gupta, A., and Jain, R. 2000. Content-based image retrieval at the end of the early years. IEEE Trans. Patt. Anal. Mach. Intell. 22, 12, 1349--1380. Google Scholar
Digital Library
- van Leuken, R. H., Garcia, L., Olivares, X., and Van Zwol, R. 2009. Visual diversification of image search results. In Proceedings of the 18th International Conference on World Wide Web (WWW’09). 341--350. Google Scholar
Digital Library
- Wang, J., Sun, J., Quan, L., Tang, X., and Shum, H. 2006. Picture collage. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition.Google Scholar
- Wang, M., Yang, K., Hua, X. S., and Zhang, H. J. 2010. Towards a relevant and diverse search of social images. IEEE Trans. Multimedia 12, 8, 829--842. Google Scholar
Digital Library
- Wang, M., Li, H., Tao, D., Lu, K., and Wu, X. 2012a. Multimodal graph-based reranking for web image search. IEEE Trans. Image Process. 21, 11, 4649--4661.Google Scholar
Digital Library
- Wang, M., Ni, B., and X. S. Hua, T. S. C. 2012b. Assistive tagging: A survey of multimedia tagging with human-computer joint exploration. ACM Comput. Surv. 44, 4. Google Scholar
Digital Library
- Wang, M., Gao, Y., Lu, K., and Rui, Y. 2013. View-based discriminative probabilistic modeling for 3d object retrieval and recognition. IEEE Trans. Image Process. 22, 4, 1395--1407.Google Scholar
Digital Library
- Xie, X., Lu, L., Jia, M., Li, H., Seide, F., and Ma, W. Y. 2008. Mobile search with multimodal queries. Proc. IEEE 96, 4, 589--601.Google Scholar
Cross Ref
- Xu, H., Wang, J., Hua, X. S., and Li, S. 2011. Hybrid image summarization. In Proceedings of the ACM International Conference on Multimedia. Google Scholar
Digital Library
- Zha, Z. J., Yang, L., Mei, T., Wang, M., and Wang, Z. 2009. Visual query suggestion. In Proceedings of the ACM International Conference on Multimedia. Google Scholar
Digital Library
Index Terms
When Amazon Meets Google: Product Visualization by Exploring Multiple Web Sources
Recommendations
Real time google and live image search re-ranking
MM '08: Proceedings of the 16th ACM international conference on MultimediaNowadays, web-scale image search engines (e.g. Google, Live Image Search) rely almost purely on surrounding text features. This leads to ambiguous and noisy results. We propose to use adaptive visual similarity to re-rank the text-based search results. ...
Why People Search for Images using Web Search Engines
WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data MiningWhat are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image ...






Comments