Texture Recognition through Machine Learning and Concept Optimiziation

This paper justifies and demonstrates a machine learning approach to the problem of texture recognition. The learning-based texture recognition is separated into the following phases: (i) the acquisition of texture concepts, (ii) the optimization of concept prototypes, and (iii) the recognition of unknown texture samples. Methodology adapted to the acquisition and recognition of-noisy texture data are introduced based on the AQ learning-from-examples algorithm. Characteristics of learning-based recognition of texture concepts are presented for different parameters of attribute extraction, different number of training data, and for different setting of the learning tooL Special emphasis is given to the optimization of noisy te.xture concepts. The optimization model and processes are designed to improve system recognition effectiveness according to given optimization criteria and evaluation measures. These criteria and measures are designed regarding the texture recognition and segmentation tasks. Various concept optimization methods are presented and tested. The empirical evaluation of developed learning-based approach to texture recognition is-demonstrated on the domain composed of twelve texture classes. Additionally, the effectiveness of a genetic search when applied to improve the worst performing concept descriptions is also presented.