Genetic programming based feature construction methods for foreground object segmentation
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- @Article{LIANG:2020:EAAI,
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author = "Jiayu Liang and Yu Xue and Jianming Wang",
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title = "Genetic programming based feature construction methods
for foreground object segmentation",
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journal = "Engineering Applications of Artificial Intelligence",
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volume = "89",
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pages = "103334",
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year = "2020",
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ISSN = "0952-1976",
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DOI = "doi:10.1016/j.engappai.2019.103334",
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URL = "http://www.sciencedirect.com/science/article/pii/S0952197619302799",
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keywords = "genetic algorithms, genetic programming, Feature
construction, Foreground object segmentation, Bloat
control",
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abstract = "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
figure-ground 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",
- }
Genetic Programming entries for
Jiayu Liang
Yu Xue
Jianming Wang
Citations