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

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

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%.

Feature similarity measurement of cross-age face images based on a deep learning algorithm

2021 ◽  
Vol 13 (2/3) ◽  
pp. 272
Author(s):  
Dong yuan Ge ◽  
Xi fan Yao ◽  
Wen jiang Xiang ◽  
En chen Liu ◽  
Zhi bin Xu
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Similarity Measurement ◽  
Deep Learning Algorithm ◽  
Face Images ◽  
Feature Similarity

An enhanced cosine-based visual technique for the robust tweets data clustering

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Narasimhulu K ◽  
Meena Abarna KT ◽  
Sivakumar B
Keyword(s):  
Crucial Role ◽  
Data Clustering ◽  
Topic Models ◽  
Content Type ◽  
Bag Of Features ◽  
Document Frequency ◽  
Visual Technique ◽  
Processing Step ◽  
Health Related

PurposeThe purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents, which is useful for achieving the robust tweets data clustering results.Design/methodology/approachLet “N” be the number of tweets documents for the topics extraction. Unwanted texts, punctuations and other symbols are removed, tokenization and stemming operations are performed in the initial tweets pre-processing step. Bag-of-features are determined for the tweets; later tweets are modelled with the obtained bag-of-features during the process of topics extraction. Approximation of topics features are extracted for every tweet document. These set of topics features of N documents are treated as multi-viewpoints. The key idea of the proposed work is to use multi-viewpoints in the similarity features computation. The following figure illustrates multi-viewpoints based cosine similarity computation of the five tweets documents (here N = 5) and corresponding documents are defined in projected space with five viewpoints, say, v1,v2, v3, v4, and v5. For example, similarity features between two documents (viewpoints v1, and v2) are computed concerning the other three multi-viewpoints (v3, v4, and v5), unlike a single viewpoint in traditional cosine metric.FindingsHealthcare problems with tweets data. Topic models play a crucial role in the classification of health-related tweets with finding topics (or health clusters) instead of finding term frequency and inverse document frequency (TF–IDF) for unlabelled tweets.Originality/valueTopic models play a crucial role in the classification of health-related tweets with finding topics (or health clusters) instead of finding TF-IDF for unlabelled tweets.

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