Image Annotation Based on Feature Weight Selection

2008 ◽  
Author(s):  
Tianzhong Zhao ◽  
Jianjiang Lu ◽  
Yafei Zhang ◽  
Qi Xiao
Keyword(s):  
Image Annotation ◽  
Feature Weight

Co-Training Framework for Feature Weight Optimization of Statistic Machine Translation

2014 ◽  
Vol 23 (12) ◽  
pp. 3101-3114
Author(s):  
Shu-Jie LIU ◽  
Chi-Ho LI ◽  
Mu LI ◽  
Ming ZHOU
Keyword(s):  
Machine Translation ◽  
Weight Optimization ◽  
Feature Weight

Image Annotation with Bayesian Universal Background Model

2013 ◽  
Vol 39 (10) ◽  
pp. 1674
Author(s):  
Dong YANG ◽  
Xiu-Ling ZHOU ◽  
Ping GUO
Keyword(s):  
Image Annotation ◽  
Background Model ◽  
Universal Background Model

Influence of feature weight on text categorization performance of Bayesian classifier

2009 ◽  
Vol 28 (12) ◽  
pp. 3080-3083 ◽  
Author(s):  
Xiu-mei GAO ◽  
Fang CHEN ◽  
Feng-xi SONG ◽  
Zhong JIN
Keyword(s):  
Text Categorization ◽  
Bayesian Classifier ◽  
Feature Weight

scholarly journals Automatic Hip Detection in Anteroposterior Pelvic Radiographs—A Labelless Practical Framework

2021 ◽  
Vol 11 (6) ◽  
pp. 522
Author(s):  
Feng-Yu Liu ◽  
Chih-Chi Chen ◽  
Chi-Tung Cheng ◽  
Cheng-Ta Wu ◽  
Chih-Po Hsu ◽  
...  
Keyword(s):  
Medical Image ◽  
Image Annotation ◽  
Region Of Interest ◽  
Confidence Score ◽  
Heterogeneous Data ◽  
Single Shot ◽  
Hip Joints ◽  
Pelvic Radiographs ◽  
Wide Range ◽  
Average Confidence

Automated detection of the region of interest (ROI) is a critical step in the two-step classification system in several medical image applications. However, key information such as model parameter selection, image annotation rules, and ROI confidence score are essential but usually not reported. In this study, we proposed a practical framework of ROI detection by analyzing hip joints seen on 7399 anteroposterior pelvic radiographs (PXR) from three diverse sources. We presented a deep learning-based ROI detection framework utilizing a single-shot multi-box detector with a customized head structure based on the characteristics of the obtained datasets. Our method achieved average intersection over union (IoU) = 0.8115, average confidence = 0.9812, and average precision with threshold IoU = 0.5 (AP50) = 0.9901 in the independent testing set, suggesting that the detected hip regions appropriately covered the main features of the hip joints. The proposed approach featured flexible loose-fitting labeling, customized model design, and heterogeneous data testing. We demonstrated the feasibility of training a robust hip region detector for PXRs. This practical framework has a promising potential for a wide range of medical image applications.

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