Genetic programming-based learning of texture classification descriptors from Local Edge Signature
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- @Article{GHAZOUANI:2020:ESA,
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author = "Haythem Ghazouani and Walid Barhoumi",
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title = "Genetic programming-based learning of texture
classification descriptors from Local Edge Signature",
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journal = "Expert Systems with Applications",
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volume = "161",
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pages = "113667",
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year = "2020",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2020.113667",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957417420304917",
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keywords = "genetic algorithms, genetic programming, Texture
classification, Texture descriptor, Feature extraction,
Local Edge Signature",
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abstract = "Describing texture is a very challenging problem for
many image-based expert and intelligent systems (e.g.
defective product detection, people re-identification,
abnormality investigation in medical imaging and remote
sensing applications) since the process of texture
classification relies on the quality of the extracted
features. Indeed, detecting and extracting features is
a hard and time-consuming task that requires the
intervention of an expert, notably when dealing with
challenging textures. Thus, machine learning-based
descriptors have emerged as another alternative to deal
with the difficulty of feature extracting. In this
work, we propose a new operator, which we named Local
Edge Signature (LES) descriptor, to locally represent
texture. The proposed texture descriptor is based on
statistical information on edge pixels' arrangement and
orientation in a specific local region, and it is
insensitive to rotation and scale changes. A genetic
programming-based approach is then fitted to
automatically learn a global texture descriptor that we
called Genetic Texture Signature (GTS). In fact, a tree
representation of individuals is used to generate
global texture features by applying elementary
operations on LES elements at a set of keypoints, and a
fitness function evaluates the descriptors considering
intra-class homogeneity and inter-class discrimination
properties of their generated features. The obtained
results, on six challenging texture datasets (Brodatz,
Outex_TC_00000, Outex_TC_00013, KTH-TIPS, KTH-TIPS2b
and UIUCTex), show that the proposed classification
method, which is fully automated, achieves
state-of-the-art performance, especially when the
number of available training samples is limited",
- }
Genetic Programming entries for
Haythem Ghazouani
Walid Barhoumi
Citations