Improving Recognition Effectiveness of Noisy Texture Concepts

Learning from noisy engineering data requires to develop either noise-tolerant learning tools or concept optimization methods. We present and compare several concept optimization approaches. This comparison is provided for texture recognition and image segmentation task. Training and test data represent 12 texture classes extracted from low quality images incorporating Laws' energy masks.

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