Genetic programming based feature construction methods for foreground object segmentation

Foreground object segmentation is a crucial preprocessing step for many high-level computer vision tasks, e.g. object recognition. It is still challenging to achieve accurate segmentation, especially for complex images (e.g. with high variations). Feature construction can help to improve the segmentation performance by extracting more distinctive features for foreground/background regions from the original features. However, commonly-used feature construction methods (e.g. principle component analysis) often involve certain assumptions/constraints, and the constructed features cannot be interpreted. To address these problems, genetic programming (GP) is employed in this paper, which is a well-suited feature construction technique. The aim of this work is to design new feature construction methods using GP, and analyse/compare popular GP-based feature construction methods for foreground object segmentation, especially on complex image datasets with high variations. Specifically, one new feature construction method that incorporates the subtree technique in GP is designed, which can construct multiple features simultaneously (called SubtMFC, Subtree Multiple Feature Construction). Moreover, a parsimony pressure technique is introduced to improve SubtMFC for bloat control (a common issue for GP-based methods), which forms the method, PSubtMFC (Parsimony SubtMFC). In addition, comparison of popular GP-based feature construction methods for foreground object segmentation is conducted for the first time. Results show that SubtMFC achieves better or similar performance compared with three reference methods. In addition, compared with SubtMFC that does not control bloat, PSubtMFC can significantly reduce the solution size while maintain similar performance in the segmentation accuracy. The GP-based feature construction framework is further extended for feature representation based knowledge transfer, which can handle the problem of the scare labelled training data. Moreover, after GP is thoroughly investigated on benchmark datasets with one type of foreground objects (i.e. the Weizmann horse dataset and Pascal aeroplane dataset), it is considered whether the GP methods can perform well on datasets containing multiple types of foreground objects. Compared with three other well-performing GP-based feature construction methods, the proposed method achieves better or comparable results for the given segmentation tasks. In addition, this paper thoroughly compares/analyses popular GP-based feature construction methods for complex figureground segmentation for the first time. Moreover, further analyses on the input features frequently used by the GP-evolved feature construction functions reflect the effectiveness of the extracted high-level features.