Automatic construction of image transformation algorithms using feature based genetic image network

Nakano, Yuta; Shirakawa, Shinichi; Yata, Noriko; Nagao, Tomoharu

doi:10.1109/cec.2010.5585981

Cited by 4 publications

(1 citation statement)

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“…An approach with similar motivations is the work by Shirakawa, Nakano et al [17,23], where two different kinds of GP networks of image operators are applied in a network to images to produce more processed image transforms. Both their work and our own works directly on the pixel space, allowing GP to find transformations useful in highlighting regions of interest.…”

Section: Reviewmentioning

confidence: 99%

The unconstrained automated generation of cell image features for medical diagnosis

Kowaliw¹,

Banzhaf

2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

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An extension to a non-linear offline method for generating features for image recognition is introduced. It aims at generating low-level features automatically when provided with some arbitrary image database. First, a general representation of prioritized pixel-neighbourhoods is described. Next, genetic programming is used to specify functions on those representations. The result is a set of transformations on the space of grayscale images. These transforms are utilized as a step in a classification process, and evolved in an evolutionary algorithm. The technique is shown to match the efficiency of the state-of-the-art on a medical image classification task. Further, the approach is shown to self-select an appropriate solution structure and complexity. Finally, we show that competitive co-evolution is a viable means of combating over-fitting. It is concluded that the technique generally shows good promise for the creation of novel image features in situations where pixel-level features are complex or unknown, such as medical images.

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Section: Reviewmentioning

confidence: 99%