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Genetic Programming With Image-Related Operators and a Flexible Program Structure for Feature Learning in Image Classification

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 (GP) with a flexible representation can find the best solution without the use of domain knowledge. This article proposes a new GP-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 visualization of the learned features provide deep insights on the proposed approach.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.25 No.1 February 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 87-101

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

REPRESENTATION
FEATURE LEARNING
GENETIC PROGRAMMING (GP)
IMAGE CLASSIFICATION
EVOLUTIONARY COMPUTATION (EC)

Info Detail Spesifik

DOI: 10.1109/TEVC.2020.3002229

Pernyataan Tanggungjawab

Ying Bi, Bing Xue, Mengjie Zhang

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