A method of evolving novel feature extraction algorithms for detecting buried objects in FLIR imagery using genetic programming

A. Paino; J. Keller; M. Popescu; K. Stone

doi:10.1117/12.2049950

9 June 2014 A method of evolving novel feature extraction algorithms for detecting buried objects in FLIR imagery using genetic programming

A. Paino, J. Keller, M. Popescu, K. Stone

Author Affiliations +

Proceedings Volume 9072, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX; 907218 (2014) https://doi.org/10.1117/12.2049950
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

In this paper we present an approach that uses Genetic Programming (GP) to evolve novel feature extraction algorithms for greyscale images. Our motivation is to create an automated method of building new feature extraction algorithms for images that are competitive with commonly used human-engineered features, such as Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). The evolved feature extraction algorithms are functions defined over the image space, and each produces a real-valued feature vector of variable length. Each evolved feature extractor breaks up the given image into a set of cells centered on every pixel, performs evolved operations on each cell, and then combines the results of those operations for every cell using an evolved operator. Using this method, the algorithm is flexible enough to reproduce both LBP and HOG features. The dataset we use to train and test our approach consists of a large number of pre-segmented image “chips” taken from a Forward Looking Infrared Imagery (FLIR) camera mounted on the hood of a moving vehicle. The goal is to classify each image chip as either containing or not containing a buried object. To this end, we define the fitness of a candidate solution as the cross-fold validation accuracy of the features generated by said candidate solution when used in conjunction with a Support Vector Machine (SVM) classifier. In order to validate our approach, we compare the classification accuracy of an SVM trained using our evolved features with the accuracy of an SVM trained using mainstream feature extraction algorithms, including LBP and HOG.

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A. Paino, J. Keller, M. Popescu, and K. Stone "A method of evolving novel feature extraction algorithms for detecting buried objects in FLIR imagery using genetic programming", Proc. SPIE 9072, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX, 907218 (9 June 2014); https://doi.org/10.1117/12.2049950

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KEYWORDS

Feature extraction

Forward looking infrared

Genetics

Computer programming

Image filtering

Target detection

Cameras

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