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R-LAIR: Riverside Lab for Artificial Intelligence Research

Re-Ranking using Compression-based Distance Measure for Content-based Commercial Product Image Retrieval (2012)

by Lunshao Chai, Zhen Qin, Honggang Zhang, Jun Guo, and Christian R. Shelton

Abstract: With the prevalence of E-Commerce sites such as eBay, Content-based Commercial Product Image Retrieval (CBCPIR) has become an emerging application-oriented field of Content- based Image Retrieval (CBIR). Though a number of traditional CBIR techniques and evaluation criterions have been applied directly or with minor modifications, they tend to neglect one critical factor that greatly affects user experience: users usually care about the exact ranks of the results, especially few top ones, which should share very high similarity with the query image. In this work, we propose a novel two-stage retrieval framework that uses a compression-based re-ranking method and a new subjective retrieval evaluation criterion to address such a problem. More specifically, we extend the state-of-art texture descriptor Campana-Keogh (CK) method from data mining in several aspects and validate the superiority of our framework via extensive experiments and real-world user feedback. We also make our code and CBCPIR dataset publicly available. The number of images of the latter is much larger than current freely accessible ones and better represents real-world commercial product images.


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Lunshao Chai, Zhen Qin, Honggang Zhang, Jun Guo, and Christian R. Shelton (2012). "Re-Ranking using Compression-based Distance Measure for Content-based Commercial Product Image Retrieval." IEEE International Conference on Image Processing. pdf      

Bibtex citation

@inproceedings{Chaetal12,
   author = "Lunshao Chai and Zhen Qin and Honggang Zhang and Jun Guo and Christian R. Shelton",
   title = "Re-Ranking using Compression-based Distance Measure for Content-based Commercial Product Image Retrieval",
   booktitle = "IEEE International Conference on Image Processing",
   booktitleabbr = "ICIP",
   year = 2012,
}

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