Texture Feature-Based Image Classification Using Wavelet Package Transform

2005 ◽  
pp. 165-173 ◽  
Author(s):  
Yue Zhang ◽  
Xing-Jian He ◽  
Jun-Hua Han
Keyword(s):  
Image Classification ◽  
Texture Feature ◽  
Feature Based

scholarly journals High Dimensional Feature for Hyperspectral Image Classification

2018 ◽  
Vol 246 ◽  
pp. 03041
Author(s):  
Cailing Wang ◽  
Hongwei Wang ◽  
Yinyong Zhang ◽  
Jia Wen ◽  
Fan Yang
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Texture Feature ◽  
High Dimensional ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Feature Based ◽  
Pca Method ◽  
The One ◽  
Low Dimensional

Making a high dimensional (e.g., 100k-dim) feature for hyperspectral image classification seems not a good idea because it will bring difficulties on consequent training, computation, and storage. In this paper, we study the performance of a high-dimensional feature by texture feature. The texture feature based on multi-local binary pattern descriptor, can achieve significant improvements over both its tradition version and the one we proposed in our previous work. We also make the high-dimensional feature practical, we employ the PCA method for dimension reduction and support vector machine for hyperspectral image classification. The two real hyperspectral image datasets are employed. Our experimental results with real hyperspectral images indicate that the high dimensional feature can enhance the classification accuracy than some low dimensional.

scholarly journals Texture feature based automated seeded region growing in abdominal MRI segmentation

2009 ◽  
Vol 02 (01) ◽  
pp. 1-8 ◽  
Author(s):  
Jie Wu ◽  
Skip Poehlman ◽  
Michael D. Noseworthy ◽  
Markad V. Kamath
Keyword(s):  
Texture Feature ◽  
Region Growing ◽  
Mri Segmentation ◽  
Seeded Region Growing ◽  
Abdominal Mri ◽  
Feature Based

scholarly journals Extracting image features for classification by two-tier genetic programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
A Song ◽  
K Neshatian ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Image Classification ◽  
Domain Knowledge ◽  
Extraction Process ◽  
High Accuracy ◽  
Classification Performance ◽  
Image Features ◽  
Classification Methods ◽  
Feature Based ◽  
Second Tier

Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem dependent and often domain knowledge is required. To address these issues we introduce a Genetic Programming (GP) based image classification method, Two-Tier GP, which directly operates on raw pixels rather than features. The first tier in a classifier is for automatically defining features based on raw image input, while the second tier makes decision. Compared to conventional feature based image classification methods, Two-Tier GP achieved better accuracies on a range of different tasks. Furthermore by using the features defined by the first tier of these Two-Tier GP classifiers, conventional classification methods obtained higher accuracies than classifying on manually designed features. Analysis on evolved Two-Tier image classifiers shows that there are genuine features captured in the programs and the mechanism of achieving high accuracy can be revealed. The Two-Tier GP method has clear advantages in image classification, such as high accuracy, good interpretability and the removal of explicit feature extraction process. © 2012 IEEE.

An ensemble feature method for food classification

2017 ◽  
Vol 26 (1) ◽  
pp. 13-39
Author(s):  
Niki Martinel ◽  
Christian Micheloni ◽  
Claudio Piciarelli
Keyword(s):  
Random Forest ◽  
State Of The Art ◽  
Texture Feature ◽  
Random Forest Classifier ◽  
Texture Feature Extraction ◽  
Feature Encoding ◽  
Food Recognition ◽  
Feature Based ◽  
Food Image ◽  
Comparative Results

In the last years, several works on automatic image-based food recognition have been proposed, often based on texture feature extraction and classification. However, there is still a lack of proper comparisons to evaluate which approaches are better suited for this specific task. In this work, we adopt a Random Forest classifier to measure the performances of different texture filter banks and feature encoding techniques on three different food image datasets. Comparative results are given to show the performance of each considered approach, as well as to compare the proposed Random Forest classifiers with other feature-based state-of-the-art solutions.

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