Bag-of-Features Based Classification of Breast Parenchymal Tissue in the Mammogram via Jointly Selecting and Weighting Visual Words

2011 ◽  
Author(s):  
Jingyan Wang ◽  
Yongping Li ◽  
Ying Zhang ◽  
Honglan Xie ◽  
Chao Wang
Keyword(s):  
Visual Words ◽  
Parenchymal Tissue ◽  
Bag Of Features

scholarly journals Ensemble-based Bag of Features for Automated Classification of Normal and COVID-19 CXR Images

2021 ◽  
pp. 102656
Author(s):  
Amira S. Ashour ◽  
Merihan M. Eissa ◽  
Maram A. Wahba ◽  
Radwa A. Elsawy ◽  
Hamada Fathy Elgnainy ◽  
...  
Keyword(s):  
Automated Classification ◽  
Bag Of Features

Explainable Fully Connected Visual Words for the Classification of Skin Cancer Confocal Images

2020 ◽  
Author(s):  
Athanasios Kallipolitis ◽  
Alexandros Stratigos ◽  
Alexios Zarras ◽  
Ilias Maglogiannis
Keyword(s):  
Skin Cancer ◽  
Visual Words ◽  
Confocal Images ◽  
Fully Connected

Object Classification of Aerial Images With Bag-of-Visual Words

2010 ◽  
Vol 7 (2) ◽  
pp. 366-370 ◽  
Author(s):  
Sheng Xu ◽  
Tao Fang ◽  
Deren Li ◽  
Shiwei Wang
Keyword(s):  
Object Classification ◽  
Aerial Images ◽  
Bag Of Visual Words ◽  
Visual Words

scholarly journals Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor

2020 ◽  
Vol 10 (7) ◽  
pp. 2525 ◽  
Author(s):  
Md Junayed Hasan ◽  
Jaeyoung Kim ◽  
Cheol Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Nearest Neighbor ◽  
Selection Process ◽  
Health State ◽  
K Nearest Neighbor ◽  
Bag Of Features ◽  
State Classification ◽  
Spherical Tank ◽  
Art Works ◽  
Health State Classification

Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features and (b) the time and frequency based statistical features. A wrapper-based feature chooser algorithm, Boruta, is utilized to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the multi-class k-nearest neighbor (k-NN) algorithm to differentiate among normal condition (NC) and two faulty conditions (FC1 and FC2). Experimental results demonstrate that the proposed methodology generates an average 99.7% accuracy for all working conditions. Moreover, it outperforms the existing state-of-art works by achieving at least 19.4%.

Classification of melanoma based on feature similarity measurement for codebook learning in the bag-of-features model

2019 ◽  
Vol 51 ◽  
pp. 200-209 ◽  
Author(s):  
Kai Hu ◽  
Xiaorui Niu ◽  
Si Liu ◽  
Yuan Zhang ◽  
Chunhong Cao ◽  
...  
Keyword(s):  
Similarity Measurement ◽  
Bag Of Features ◽  
Feature Similarity ◽  
Codebook Learning
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