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Content Provider | ACM Digital Library |
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Author | Kowaliw, Taras Banzhaf, Wolfgang |
Abstract | 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. |
Starting Page | 1103 |
Ending Page | 1110 |
Page Count | 8 |
File Format | |
ISBN | 9781450311779 |
DOI | 10.1145/2330163.2330316 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2012-07-07 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Image processing Tef Genetic programming Co-evolution Pattern recognition Muscular dystrophy |
Content Type | Text |
Resource Type | Article |
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