Histopathology Image Classification Using Bag of Features and Kernel Functions

2009 ◽  
pp. 126-135 ◽  
Author(s):  
Juan C. Caicedo ◽  
Angel Cruz ◽  
Fabio A. Gonzalez
Keyword(s):  
Image Classification ◽  
Kernel Functions ◽  
Bag Of Features

Image Normalization and Weighted Classification Using an Efficient Approach for SVM Classifiers

2020 ◽  
Vol 20 (04) ◽  
pp. 2050035
Author(s):  
Sumit Dhariwal ◽  
Sellappan Palaniappan
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Error Rate ◽  
Kernel Functions ◽  
Support Vector ◽  
Image Normalization ◽  
Novel Approach ◽  
Low Levels ◽  
To Come

The content of massive image changing the brightest brightness is an impasse between most tests of sorted image realizations with low-resolution representation. I have done this research through image security, which will help curb crime in the coming days, and we propose a novel receipt for their strong and effective counterpart. Image classification using low levels of the image is a difficult method, so for this, I have adopted the method of automating the semantic image classification of this research and used it with different SVM classifiers, based on the normalized weighted feature support vector machine for semantic image classification. This is a novel approach given that weighted feature or normalized biased feature is applied and it is found that the normalized method is the best. It also uses normalized weighted features to compute kernel functions and train SVM. The trained SVM is then used to classify new images. During training and generalization, we displayed a decrease of identification error rate and there have been many benefits of using SVM with better performance in normalized image-cataloging systems. The importance of this technique and its role will be highlighted in the years to come.

BMAX: A Bag of Features Based Method for Image Classification

2015 ◽  
Author(s):  
Pedro Senna ◽  
Isabela Neves Drummond ◽  
Guilherme Sousa Bastos
Keyword(s):  
Image Classification ◽  
Bag Of Features

A Review on Image Classification Using Bag of Features Approach

2019 ◽  
Vol 7 (6) ◽  
pp. 538-542
Author(s):  
Santosh Kumar Panda ◽  
Chandra Sekhar Panda
Keyword(s):  
Image Classification ◽  
Bag Of Features

Bag-of-Features in Microscopic Images Classification

2016 ◽  
pp. 1-22
Author(s):  
Samsad Beagum ◽  
Amira S. Ashour ◽  
Nilanjan Dey
Keyword(s):  
Image Classification ◽  
Binary Classification ◽  
Support Vector ◽  
Albino Rats ◽  
Microscopic Image ◽  
Microscopic Images ◽  
Bag Of Features ◽  
Leading Role ◽  
Average Accuracy ◽  
Quantitative Image

Microscopic image analysis plays a foremost role for understanding biological processes, diagnosis of diseases and cells/ tissues identification. Microscopic image classification is one of the challenging tasks that have a leading role in the medical domain. In this chapter, an overview on different classification techniques elaborated with microscopic images is presented to guide the reader through the advanced knowledge of major quantitative image classification approaches. Applied examples are conducted to classify different Albino rats' samples captured using light microscope for three different organs, namely hippocampus, renal and pancreas. The Bag-of-Features (BoF) technique was employed for features extraction and selection. The BoF selected features were used as input to the multiclass linear support vector machine classifier. The proposed classifier achieved 94.33% average classification accuracy for the three classes. Additionally, for binary classification the achieved average accuracy was 100% for hippocampus and pancreas sets classification.

Bag-of-Features in Microscopic Images Classification

2017 ◽  
pp. 435-456
Author(s):  
Samsad Beagum ◽  
Amira S. Ashour ◽  
Nilanjan Dey
Keyword(s):  
Image Classification ◽  
Binary Classification ◽  
Support Vector ◽  
Albino Rats ◽  
Microscopic Image ◽  
Microscopic Images ◽  
Bag Of Features ◽  
Leading Role ◽  
Average Accuracy ◽  
Quantitative Image

Microscopic image analysis plays a foremost role for understanding biological processes, diagnosis of diseases and cells/ tissues identification. Microscopic image classification is one of the challenging tasks that have a leading role in the medical domain. In this chapter, an overview on different classification techniques elaborated with microscopic images is presented to guide the reader through the advanced knowledge of major quantitative image classification approaches. Applied examples are conducted to classify different Albino rats' samples captured using light microscope for three different organs, namely hippocampus, renal and pancreas. The Bag-of-Features (BoF) technique was employed for features extraction and selection. The BoF selected features were used as input to the multiclass linear support vector machine classifier. The proposed classifier achieved 94.33% average classification accuracy for the three classes. Additionally, for binary classification the achieved average accuracy was 100% for hippocampus and pancreas sets classification.

scholarly journals Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting

2012 ◽  
Vol 1 (1) ◽  
pp. 63 ◽  
Author(s):  
Ankush Chakrabarty ◽  
Olivia Choudhury ◽  
Pallab Sarkar ◽  
Avishek Paul ◽  
Debarghya Sarkar
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Kernel Functions ◽  
Hyperspectral Data ◽  
Superior Performance ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Bacterial Foraging Optimization ◽  
Imaging Spectrometer ◽  
Bacterial Foraging

The present paper describes the development of a hyperspectral image classification scheme using support vector machines (SVM) with spectrally weighted kernels. The kernels are designed during the training phase of the SVM using optimal spectral weights estimated using the Bacterial Foraging Optimization (BFO) algorithm, a popular modern stochastic optimization algorithm. The optimized kernel functions are then in the SVM paradigm for bi-classification of pixels in hyperspectral images. The effectiveness of the proposed approach is demonstrated by implementing it on three widely used benchmark hyperspectral data sets, two of which were taken over agricultural sites at Indian Pines, Indiana, and Salinas Valley, California, by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) at NASA’s Jet Propulsion Laboratory. The third dataset was acquired using the Reflective Optical System Imaging Spectrometer (ROSIS) over an urban scene at Pavia University, Italy to demonstrate the efficacy of the proposed approach in an urban scenario as well as with agricultural data. Classification errors for One-Against-One (OAO) and classification accuracies for One-Against-All (OAA) schemes were computed and compared to other methods developed in recent times. Finally, the use of the BFO-based technique is recommended owing to its superior performance, in comparison to other contemporary stochastic bio-inspired algorithms.

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