An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming

Abstract

© Springer International Publishing AG, part of Springer Nature 2018. Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification.

Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification

“…The 3TGP method has an image filtering tier, an aggregation tier and a classification tier, which performs image filtering, region detection, feature extraction, feature construction, and classification in a single tree. Bi et al [29] proposed a multi-layer GP method to achieve region detection, feature extraction, feature construction layer, and classification simultaneously. However, these representations are only effective for solving binary image classification tasks because the output of each GP tree is a floating-point number, which is naturally suitable for binary classification.…”