scholarly journals Contextual Hierarchical Part-Driven Conditional Random Field Model for Object Category Detection

2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
Lizhen Wu ◽  
Yifeng Niu ◽  
Lincheng Shen
Keyword(s):  
Random Field ◽  
Conditional Random Field ◽  
Neighborhood Structure ◽  
Object Category ◽  
Individual Object ◽  
Conditional Random Field Model ◽  
Object Part ◽  
Object Parts ◽  
Object Models ◽  
Contextual Interactions

Even though several promising approaches have been proposed in the literature, generic category-level object detection is still challenging due to high intraclass variability and ambiguity in the appearance among different object instances. From the view of constructing object models, the balance between flexibility and discrimination must be taken into consideration. Motivated by these demands, we propose a novel contextual hierarchical part-driven conditional random field (CRF) model, which is based on not only individual object part appearance but also model contextual interactions of the parts simultaneously. By using a latent two-layer hierarchical formulation of labels and a weighted neighborhood structure, the model can effectively encode the dependencies among object parts. Meanwhile, beta-stable local features are introduced as observed data to ensure the discriminative and robustness of part description. The object category detection problem can be solved in a probabilistic framework using a supervised learning method based on maximum a posteriori (MAP) estimation. The benefits of the proposed model are demonstrated on the standard dataset and satellite images.

scholarly journals Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Yibo Li ◽  
Yuxiang Zhang ◽  
Huiyu Zhu ◽  
Rongxin Yan ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Random Field ◽  
Storage Tank ◽  
Field Model ◽  
Conditional Random Field ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Random Field Model ◽  
Conditional Random Field Model

Acoustic emission (AE) technique is often used to detect inaccessible area of large storage tank floor with AE sensors placed outside the tank. For tanks with fixed roofs, the drop-back signals caused by condensation mix with corrosion signals from the tank floor and interfere with the online AE inspection. The drop-back signals are very difficult to filter out using conventional methods. To solve this problem, a novel AE inner detector, which works inside the storage tank, is adopted and a pattern recognition algorithm based on CRF (Conditional Random Field) model is presented. The algorithm is applied to differentiate the corrosion signals from interference signals, especially drop-back signals caused by condensation. Q235 steel corrosion signals and drop-signals were collected both in laboratory and in field site, and seven typical AE features based on hits and frequency are extracted and selected by mRMR (Minimum Redundancy Maximum Relevance) for pattern recognition. To validate the effectiveness of the proposed algorithm, the recognition result of CRF model was compared with BP (Back Propagation), SVM (Support Vector Machine), and HMM (Hidden Markov Model). The results show that training speed, accuracy, and ROC (Receiver Operating Characteristic) results of the CRF model outperform other methods.

scholarly journals Linear street extraction using a Conditional Random Field model

2015 ◽  
Vol 14 ◽  
pp. 532-545 ◽  
Author(s):  
Padraig Corcoran ◽  
Peter Mooney ◽  
Michela Bertolotto
Keyword(s):  
Random Field ◽  
Field Model ◽  
Conditional Random Field ◽  
Random Field Model ◽  
Conditional Random Field Model

scholarly journals An ensemble of neural models for nested adverse drug events and medication extraction with subwords

2019 ◽  
Vol 27 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Meizhi Ju ◽  
Nhung T H Nguyen ◽  
Makoto Miwa ◽  
Sophia Ananiadou
Keyword(s):  
Random Field ◽  
Adverse Drug Events ◽  
Conditional Random Field ◽  
Neural Model ◽  
Neural Models ◽  
Data Set ◽  
Conditional Random Field Model ◽  
Semantic Types ◽  
Unknown Words ◽  
Mimic Iii

Abstract Objective This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2. Materials and Methods We designed a neural model to tackle both nested (entities embedded in other entities) and polysemous entities (entities annotated with multiple semantic types) based on MIMIC III discharge summaries. To better represent rare and unknown words in entities, we further tokenized the MIMIC III data set by splitting the words into finer-grained subwords. We finally combined all the models to boost the performance. Additionally, we implemented a featured-based conditional random field model and created an ensemble to combine its predictions with those of the neural model. Results Our method achieved 92.78% lenient micro F1-score, with 95.99% lenient precision, and 89.79% lenient recall, respectively. Experimental results showed that combining the predictions of either multiple models, or of a single model with different settings can improve performance. Discussion Analysis of the development set showed that our neural models can detect more informative text regions than feature-based conditional random field models. Furthermore, most entity types significantly benefit from subword representation, which also allows us to extract sparse entities, especially nested entities. Conclusion The overall results have demonstrated that the ensemble method can accurately recognize entities, including nested and polysemous entities. Additionally, our method can recognize sparse entities by reconsidering the clinical narratives at a finer-grained subword level, rather than at the word level.

scholarly journals Improving conditional random field model for prediction of protein-RNA residue-base contacts

2018 ◽  
Vol 6 (2) ◽  
pp. 155-162
Author(s):  
Morihiro Hayashida ◽  
Noriyuki Okada ◽  
Mayumi Kamada ◽  
Hitoshi Koyano
Keyword(s):  
Random Field ◽  
Field Model ◽  
Conditional Random Field ◽  
Random Field Model ◽  
Conditional Random Field Model
Sign in / Sign up

Export Citation Format

Share Document