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Scale co-occurrence matrix (SCM) is a method for image feature extraction within scale space after wavelet transformation, proposed by Wu Jun and Zhao Zhongming (Institute of Remote Sensing Application, China). In practice, we first do discrete wavelet transformation for one gray image and get sub images with different scales. Then we construct a series of scale based concurrent matrixes, every matrix describing the gray level variation between two adjacent scales. Last we use selected functions (such as Harris statistical approach) to calculate measurements with SCM and do feature extraction and classification. One basis of the method is the fact: way texture information changes from one scale to another can represent that texture in some extent thus it can be used as a criteria for feature extraction. The matrix captures the relation of features between different scales rather than the features within a single scale space, which can represent the scale property of texture better. Also, there are several experiments showing that it can get more accurate results for texture classification than the traditional texture classification. == Background == Texture can be regarded as a similarity grouping in an image. Traditional texture analysis can be divided into four major issues: feature extraction, texture discrimination, texture classification and shape from texture(to reconstruct 3D surface geometry from texture information). For tradition feature extraction, approaches are usually categorized into structural, statistical, model based and transform. Wavelet transformation is a popular method in numerical analysis and functional analysis, which captures both frequency and location information. Gray level co-occurrence matrix provides an important basis for SCM construction. SCM based on discrete wavelet frame transformation make use of both correlations and feature information so that it combines structural and statistical benefits. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Scale co-occurrence matrix」の詳細全文を読む スポンサード リンク
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