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RI | Publications | Object-Based Image Retrieval using the Statistical Structure of Images
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Object-Based Image Retrieval using the Statistical Structure of Images
D. Hoiem, R. Sukthankar, H. Schneiderman, and L. Huston
IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, June, 2004, pp. 490 - 497.
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| Abstract |
We propose a new Bayesian approach to object-based image retrieval with relevance feedback. Although estimating the object posterior probability density from few examples seems infeasible, we are able to approximate this density by exploiting statistics of the image database domain. Unlike previous approaches that assume an arbitrary distribution for the unconditional density of the feature vector (the density of the features taken over the entire image domain), we learn both the structure and the parameters of this density. These density estimates enable us to construct a Bayesian classifier. Using this Bayesian classifier, we perform a windowed scan over images for objects of interest and employ the user’s feedback on the search results to train a second classifier that focuses on eliminating difficult false positives. We have incorporated this algorithm into an object-based image retrieval system. We demonstrate the effectiveness of our approach with experiments using a set of categories from the Corel database.
| Notes |
Sponsor: Intel Research Pittsburgh
Associated center: VASC
Associated lab/group: Face Group
Associated project: Object Recognition Using Statistical Modeling
Number of pages: 8
| Text Reference |
D. Hoiem, R. Sukthankar, H. Schneiderman, and L. Huston, "Object-Based Image Retrieval using the Statistical Structure of Images," IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, June, 2004, pp. 490 - 497.
| BibTeX Reference |
@inproceedings{Hoiem_2004_4644,
author = "Derek Hoiem and Rahul Sukthankar and Henry Schneiderman and Larry Huston",
title = "Object-Based Image Retrieval using the Statistical Structure of Images",
booktitle = "IEEE Conference on Computer Vision and Pattern Recognition",
month = "June",
year = "2004",
volume = "2",
pages = "490 - 497"
}