Classificação de cenas utilizando a análise da aleatoriedade por aproximação da complexidade de Kolmogorov
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2020-03-15
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Universidade Federal de Goiás
Resumo
In many pattern recognition problems, discriminant features are unknown and/or class boundaries are not
well defined. Several studies have used data compression to discover knowledge, without features
extraction and selection. The basic idea is two distinct objects can be grouped as similar, if the information
content of one explains, in a significant way, the information content of the other. However, compressionbased
techniques are not efficient for images, as they disregard the semantics present in the spatial
correlation of two-dimensional data. A classifier is proposed for estimates the visual complexity of scenes,
namely Pattern Recognition by Randomness (PRR). The operation of the method is based on data
transformations, which expand the most discriminating features and suppress details. The main
contribution of the work is the use of randomness as a measure discrimination. The approximation
between scenes and trained models, based on representational distortion, promotes a lossy compression
process. This loss is associated with irrelevant details, when the scene is reconstructed with the
representation of true class, or with the information degradation, when it is reconstructed with divergent
representations. The more information preserved, the greater the randomness of the reconstruction. From
the mathematical point of view, the method is explained by two main measures in the U-dimensional
plane: intersection and dispersion. The results yielded accuracy of 0.6967, for a 12-class problem, and
0.9286 for 7 classes. Compared with k-NN and a data mining toolkit, the proposed classifier was superior.
The method is capable of generating efficient models from few training samples. It is invariant for vertical
and horizontal reflections and resistant to some geometric transformations and image processing.
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FEITOSA, R. D. F. Classificação de cenas utilizando a análise da aleatoriedade por aproximação da complexidade de Kolmogorov. 2020. 204 f. Tese (Doutorado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2020.