Evolutionary image processing (EIP) is a sub-area of

digital image processing Digital image processing is the use of a digital computer to process digital images through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allo ...

Evolutionary algorithms Evolutionary algorithms (EA) reproduce essential elements of the biological evolution in a computer algorithm in order to solve "difficult" problems, at least Approximation, approximately, for which no exact or satisfactory solution methods are k ...

(EA) are used to optimize and solve various image processing problems. Evolutionary image processing thus represents the combination of evolutionary optimization and digital image processing. EAs have been used for several decades in computer science to optimize various problems. The application in image processing, on the other hand, is still a relatively new field of research. This is primarily due to the technological development of computer systems, as EIP is a relatively computationally intensive process. Evolutionary computer vision (ECV) is an application of EIP for computer vision. It has been shown that

genetic programming Genetic programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population of programs. It applies the genetic operators selection (evolutionary algorithm), selection a ...

(GP) as a subclass of EAs is particularly useful for image processing.

Genetic programming for image processing

In evolutionary image processing, genetic programming optimizes the arrangement of different image-processing operators for specific outputs or task performance. As of 2021, in comparison to popular and well developed

convolutional neural network A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different ty ...

s, GP is an emerging technique for

feature learning In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual fea ...

. In particular, GP has been used for developing accurate classifiers for

object detection Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched ...

, classification of medical images, and optical character recognition. GP has multiple advantages in case of image processing. They include: # The GP output is a program or a collection of programs in the form of mathematical expressions, which are easy to interpret after simplification and conversion to normal notation. # The GP needs considerable time for evolution of GP based classifiers. However, the resulting GP tree needs very short execution time in the testing. # GP fitness function is flexible and can be adapted according to the problem to be solved. The disadvantages of GP for image processing include: # Computational cost for evolution of GP based classifiers is very high. # A large dataset is required for the training. # Due to their stochastic nature, a solution is not guaranteed.

References

{{Digital image processing Applications of evolutionary algorithms Image processing

Genetic programming for image processing

See also

References