Statistical bigram correlation model for image retrieval

Details Statistical bigram correlation model for image retrieval Statistical bigram correlation model for image retrieval

2005-05-31

2005-05-31

Ali, Mohammad (Department: 2162)

Data processing: database and file management or data structures

Database design

Data structure types

C707S793000, C707S793000, C707S793000

Reexamination Certificate

active

06901411

ABSTRACT:
The disclosed subject matter improves iterative results of content-based image retrieval (CBIR) using a bigram model to correlate relevance feedback. Specifically, multiple images are received responsive to multiple image search sessions. Relevance feedback is used to determine whether the received images are semantically relevant. A respective semantic correlation between each of at least one pair of the images is then estimated using respective bigram frequencies. The bigram frequencies are based on multiple search sessions in which each image of a pair of images is semantically relevant.

REFERENCES:
patent: 6175829 (2001-01-01), Li et al.
patent: 6347313 (2002-02-01), Ma et al.
patent: 6480840 (2002-11-01), Zhu et al.
patent: 2003/0123737 (2003-07-01), Mojsilovic et al.
Selim Aksoy & Robert M. Haralick, “Graph-Theoretic Clustering for Image Grouping and Retrieval,” IEEE Conf. on Computer Vision and Pattern Recognition, Jun. 1999.
Ingemar J. Cox, Matt L. Miller, Thomas P. Minka, Thomas V. Papathomas, & Peter N. Yianilos, “The Bayesian Image Retrieval System, PicHunter: Theory, Implementation and Psychophysical Experiments,” IEEE Transactions on Image Processing, vol. XX, 2000, pp. 1-19.
Yong Rui, Thomas S. Huang, Sharad Mehrotra, & Michael Ortega, “A Relevance Feedback Architecture for Content-based Multimedia Information Retrieval Systems,” IFP Lab, Beckman Institute, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL.
Ye Lu, Chunhui Hu, Xingquan Zhu, HongJiang Zhang, Qiang Yang, “A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems,” Microsoft Research China, Beijing, China.
Zheng Chen, Liu Wenyin, Feng Zhang, Mingjing Li, Hongjiang Zhang, “Web Mining for Web Image Retrieval,” Microsoft Research China, Beijing, China, pp. 1-15.
Philip Clarkson & Ronald Rosenfeld, “Statistical Language Modeling Using the CMU-Cambridge Toolkit,” Cambridge University Engineering Department, Cambridge, and School of Computer Science, Carnegie Mellon University, Pittsburgh, PA.
Jing Huang, S. Ravi Kumar & Mandar Mitra, “ Combining Supervised Learning with Color Correlograms for Content-Based Image Retrieval,” Department of Computer Science, Cornell University, Ithaca, NY 14853.
Hongjiang Zhang, Liu Wenyin & Chunhui Hu, “iFind—A System for Semantics and Feature Based Image Retrieval over Internet,” Microsoft Research China, Beijing 100080, China.

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