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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Generalized manifold-ranking-based image retrieval.

Jingrui He¹, Mingjing Li, Hong-Jiang Zhang

¹Automation Department, Tsinghua University, Beijing, China. hejingrui98@mails.tsinghua.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|October 7, 2006

Summary

This summary is machine-generated.

We introduce generalized manifold-ranking-based image retrieval (gMRBIR), a flexible framework for image retrieval. This method enhances accuracy and applicability by working even when query images are not in the database.

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Area of Science:

Computer Science
Machine Learning
Image Processing

Background:

Image retrieval systems often struggle with query images not present in the database.
Existing transductive learning methods like MRBIR have limitations in real-world applicability.

Purpose of the Study:

To propose a general transductive learning framework for image retrieval that is robust to query images outside the database.
To improve the convergence speed and accuracy of image retrieval results.

Main Methods:

Developed generalized manifold-ranking-based image retrieval (gMRBIR) framework.
Utilized manifold ranking to propagate scores from a pseudo seed vector to unlabeled images.
Incorporated relevance feedback and active learning for result refinement.

Main Results:

gMRBIR demonstrates superior performance compared to state-of-the-art techniques.
The framework effectively handles query images both within and outside the initial database.
Experiments on a 5,000 Corel image dataset validate the method's effectiveness.

Conclusions:

gMRBIR offers a more applicable and robust solution for image retrieval tasks.
The integration of manifold ranking, relevance feedback, and active learning enhances retrieval efficiency and accuracy.