Image classification using adaptive-boosting and tree-structured discriminant vector quantization

2004 ◽  
Author(s):  
K.M. Ozonat ◽  
R.M. Gray
Keyword(s):  
Image Classification ◽  
Vector Quantization ◽  
Adaptive Boosting ◽  
Discriminant Vector

scholarly journals A hybrid Genetic Programming approach to feature detection and image classification

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
Bing Xue
Keyword(s):  
Genetic Programming ◽  
Image Classification ◽  
Feature Detection ◽  
Image Feature ◽  
Programming Approach ◽  
Support Vector ◽  
Adaptive Boosting ◽  
Speeded Up Robust Features ◽  
Wrapper Approach ◽  
Vector Machines

© 2015 IEEE. Image classification is a crucial task in Computer Vision. Feature detection represents a key component of the image classification process, which aims at detecting a set of important features that have the potential to facilitate the classification task. In this paper, we propose a Genetic Programming (GP) approach to image feature detection. The proposed method uses the Speeded Up Robust Features (SURF) method to extract features from regions automatically selected by GP, and adopts a wrapper approach combined with a voting scheme to perform image classification. The proposed approach is evaluated using three datasets of increasing difficulty, and is compared to five popularly used machine learning methods: Support Vector Machines, Random Forest, Naive Bayes, Decision Trees, and Adaptive Boosting. The experimental results show the proposed approach has achieved comparable or better performance than the five existing methods on all three datasets, and reveal its capability to automatically detect good regions from a large image from which good features are automatically constructed.

Grey Relational Analysis based Keypoints Selection in Bag-of-Features for Histopathological Image Classification

2019 ◽  
Vol 12 (4) ◽  
pp. 260-268 ◽  
Author(s):  
Raju Pal ◽  
Mukesh Saraswat
Keyword(s):  
Image Classification ◽  
Vector Quantization ◽  
Grey Relational Analysis ◽  
Clinical Practices ◽  
Bag Of Features ◽  
Relational Analysis ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Histopathological Image Classification ◽  
Grey Relational

Background: With the expeditious development of current medical imaging technology, the availability of histopathological images has been increased in a large number. Hence, histopathological image classification and annotation have emerged as the prime research fields in the pathological diagnosis and clinical practices. Several methods are available for the automation of image classification. Methods: Recently, the bag-of-features appeared as a successful histopathological image classification method. However, all the extracted keypoints in bag-of-features are not relevant and generally have very high dimensions, which degrade the performance of a classifier. Therefore, this paper introduces a new Grey relational analysis-based bag-of-features method to search the relevant keypoints. Results: The efficacy of the proposed method has been analyzed on animal diagnostics lab histopathological image datasets having healthy and inflamed images of three organs. The average accuracy of the proposed method is 88.3%, which is the highest among other state-of-the-art methods. Conclusion: This paper introduced a new Grey relational analysis-based bag-of-features which improves the efficiency of vector quantization step of the standard bag-of-features method. The method used Grey relational analysis for similarity measure in vector quantization method of bag-offeatures. The proposed method has been validated in terms of precision, recall, G-mean, F1 score, and radar charts on three datasets, Kidney, Lung, and Spleen of ADL histopathological images.

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