Chinese wine classification system based on micrograph using combination of shape and structure features

Yi Wan

doi:10.1117/12.896289

8 July 2011 Chinese wine classification system based on micrograph using combination of shape and structure features

Yi Wan

Proceedings Volume 8009, Third International Conference on Digital Image Processing (ICDIP 2011); 800922 (2011) https://doi.org/10.1117/12.896289
Event: 3rd International Conference on Digital Image Processing, 2011, Chengdu, China

Abstract

Chinese wines can be classification or graded by the micrographs. Micrographs of Chinese wines show floccules, stick and granule of variant shape and size. Different wines have variant microstructure and micrographs, we study the classification of Chinese wines based on the micrographs. Shape and structure of wines' particles in microstructure is the most important feature for recognition and classification of wines. So we introduce a feature extraction method which can describe the structure and region shape of micrograph efficiently. First, the micrographs are enhanced using total variation denoising, and segmented using a modified Otsu's method based on the Rayleigh Distribution. Then features are extracted using proposed method in the paper based on area, perimeter and traditional shape feature. Eight kinds total 26 features are selected. Finally, Chinese wine classification system based on micrograph using combination of shape and structure features and BP neural network have been presented. We compare the recognition results for different choices of features (traditional shape features or proposed features). The experimental results show that the better classification rate have been achieved using the combinational features proposed in this paper.

Citation Download Citation

Yi Wan "Chinese wine classification system based on micrograph using combination of shape and structure features", Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 800922 (8 July 2011); https://doi.org/10.1117/12.896289

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KEYWORDS

Photomicroscopy

Particles

Classification systems

Feature extraction

Denoising

Image segmentation

Algorithm development

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