A Probabilistic SVM Approach to Annotation of Calcification Mammograms

2012 ◽  
pp. 208-222
Author(s):  
Chia-Hung Wei ◽  
Sherry Y. Chen
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Image Annotation ◽  
Medical Images ◽  
Extraction Methods ◽  
Experimental Results ◽  
Automatic Image Annotation ◽  
Accuracy Rate ◽  
Present Feature ◽  
Average Accuracy

Due to the increasing use of digital medical images, a need exists to develop an approach for automatic image annotation, which provides textual labels for images. Thus added labels can be used to access images using textual queries. Automatic image annotation can be separated into two individual tasks: feature extraction and image classification. In this paper, the authors present feature extraction methods for calcification mammograms. The resultant features, based on BI-RADS standards, make annotated image contents represent the correct medical meaning and tag correspondent terms. Furthermore, this paper also proposes a probabilistic SVM approach to image classification. Finally, the experimental results indicate that the probabilistic SVM approach to image annotation can achieve 79.5% in the average accuracy rate.

Color Features and Color Spaces Applications to the Automatic Image Annotation

Biometrics ◽  
2017 ◽  
pp. 1061-1083
Author(s):  
Vafa Maihami ◽  
Farzin Yaghmaee
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Image Annotation ◽  
Color Histogram ◽  
Experimental Results ◽  
Automatic Image Annotation ◽  
Color Spaces ◽  
Color Features ◽  
Different Color ◽  
Color Moment

Nowadays images play a crucial role in different fields such as medicine, advertisement, education and entertainment. Describing images content and retrieving them are important fields in image processing. Automatic image annotation is a process which produces words from a digital image based on the content of this the image by using a computer. In this chapter, after an introduction to neighbor voting algorithm for image annotation, we discuss the applicability of color features and color spaces in automatic image annotation. We discuss the applicability of three color features (color histogram, color moment and color Autocorrelogram) and three color spaces (RGB, HSI and YCbCr) in image annotation. Experimental results, using Corel5k benchmark annotated images dataset, demonstrate that using different color spaces and color features helps to select the best color features and spaces in image annotation area.

scholarly journals Genetic Programming with Image-Related Operators and A Flexible Program Structure for Feature Learning in Image Classification

2020 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Image Classification ◽  
Domain Knowledge ◽  
Extraction Methods ◽  
Classification Performance ◽  
Model Complexity ◽  
Program Structure ◽  
New Approach ◽  
Classification Tasks

IEEE Feature extraction is essential for solving image classification by transforming low-level pixel values into high-level features. However, extracting effective features from images is challenging due to high variations across images in scale, rotation, illumination, and background. Existing methods often have a fixed model complexity and require domain expertise. Genetic programming with a flexible representation can find the best solution without the use of domain knowledge. This paper proposes a new genetic programming-based approach to automatically learning informative features for different image classification tasks. In the new approach, a number of image-related operators, including filters, pooling operators and feature extraction methods, are employed as functions. A flexible program structure is developed to integrate different functions and terminals into a single tree/solution. The new approach can evolve solutions of variable depths to extract various numbers and types of features from the images. The new approach is examined on 12 different image classification tasks of varying difficulty and compared with a large number of effective algorithms. The results show that the new approach achieves better classification performance than most benchmark methods. The analysis of the evolved programs/solutions and the visualisation of the learned features provide deep insights on the proposed approach.

Learning Advanced Brain Computer Interface Technology

2019 ◽  
Vol 15 (3) ◽  
pp. 14-27
Author(s):  
Wang Tao ◽  
Wu Linyan ◽  
Li Yanping ◽  
Gao Nuo ◽  
Zhang Weiran
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Support Vector ◽  
Wavelet Packet Analysis ◽  
Accuracy Rate ◽  
Desirable Outcome ◽  
Eeg Data ◽  
System Data

Feature extraction is an important step in electroencephalogram (EEG) processing of motor imagery, and the feature extraction of EEG directly affects the final classification results. Through the analysis of various feature extraction methods, this article finally selects Common Spatial Patterns (CSP) and wavelet packet analysis (WPA) to extract the feature and uses Support Vector Machine (SVM) to classify and compare these extracted features. For the EEG data provided by GRAZ University, the accuracy rate of feature extraction using CSP algorithm is 85.5%, and the accuracy rate of feature extraction using wavelet packet analysis is 92%. Then this paper analyzes the EEG data collected by Emotiv epoc+ system. The classification accuracy of wavelet packet extracted features can still be maintained at more than 80%, while the classification accuracy of CSP extracted feature is decreased obviously. Experimental results show that the method of wavelet packet analysis towards competition data and Emotiv epoc+ system data can both get a desirable outcome.

scholarly journals Genetic Programming with Image-Related Operators and A Flexible Program Structure for Feature Learning in Image Classification

2020 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Image Classification ◽  
Domain Knowledge ◽  
Extraction Methods ◽  
Classification Performance ◽  
Model Complexity ◽  
Program Structure ◽  
New Approach ◽  
Classification Tasks

IEEE Feature extraction is essential for solving image classification by transforming low-level pixel values into high-level features. However, extracting effective features from images is challenging due to high variations across images in scale, rotation, illumination, and background. Existing methods often have a fixed model complexity and require domain expertise. Genetic programming with a flexible representation can find the best solution without the use of domain knowledge. This paper proposes a new genetic programming-based approach to automatically learning informative features for different image classification tasks. In the new approach, a number of image-related operators, including filters, pooling operators and feature extraction methods, are employed as functions. A flexible program structure is developed to integrate different functions and terminals into a single tree/solution. The new approach can evolve solutions of variable depths to extract various numbers and types of features from the images. The new approach is examined on 12 different image classification tasks of varying difficulty and compared with a large number of effective algorithms. The results show that the new approach achieves better classification performance than most benchmark methods. The analysis of the evolved programs/solutions and the visualisation of the learned features provide deep insights on the proposed approach.

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