Self-tuned Evolution-COnstructed features for general object recognition

Authors:
Kirt Lillywhite;Beau Tippetts;Dah-Jye Lee
Affiliations:
Department of Electrical and Computer Engineering, Brigham Young University, 459 Clyde Building, Provo, UT 84602, USA;Department of Electrical and Computer Engineering, Brigham Young University, 459 Clyde Building, Provo, UT 84602, USA;Department of Electrical and Computer Engineering, Brigham Young University, 459 Clyde Building, Provo, UT 84602, USA
Venue:
Pattern Recognition
Year:
2012

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Abstract

Object recognition is a well studied but extremely challenging field. We present a novel approach to feature construction for object detection called Evolution-COnstructed Features (ECO features). Most current approaches rely on human experts to construct features for object recognition. ECO features are automatically constructed by uniquely employing a standard genetic algorithm to discover multiple series of transforms that are highly discriminative. Using ECO features provides several advantages over other object detection algorithms including: no need for a human expert to build feature sets or tune their parameters, ability to generate specialized feature sets for different objects, no limitations to certain types of image sources, and ability to find both global and local feature types. We show in our experiments that the ECO features compete well against state-of-the-art object recognition algorithms.