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Extracting the Best Features from an Image using Genetic Programming

عنوان مقاله: Extracting the Best Features from an Image using Genetic Programming
شناسه (COI) مقاله: ICMEAC05_131
منتشر شده در پنجمین کنفرانس بین المللی تحقیقات نوین پژوهشی در مهندسی و تکنولوژی در سال 1396
مشخصات نویسندگان مقاله:

Eisa Rezaei - M.Sc student of Software Engineering, Germi Branch, Islamic Azad University, Germi, Iran
Seyed Naser Razavi - ssistant Professor, faculty of Electrical and Computer Engineering University of Tabriz, Tabriz, Iran

خلاصه مقاله:
In this paper, we used genetic programming (GP) for feature extraction and tested the resulting program using Brodatz and Vistex images as datasets. Our inputs were the most basic level of information in the image, raw pixels. Our goal was to reach the smallest image size with the greatest number of features in order to improve classification accuracy and also time of execution and processing. To achieve this goal, we used a loop in the program to extract the image features and determine classification accuracy for different images sizes. After feature extraction, we used nearest neighbor algorithm for classification. Average of classification accuracy in our method was 86.64% for Brodatz images and 85.68% for Vistex images. We showed that our method, texture feature extraction using genetic programming, can compete with other methods.

کلمات کلیدی:
Texture, Feature Extraction, Genetic Programming, Nearest Neighbor Classification, Raw Pixel, Brodatz, Vistex

صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/749694/