What are the ways of feature definition in image recognition?

There are four ways to define features in the process of image recognition:

1, statistical method. The typical representative of statistical method is a texture feature analysis method called gray * * * generation matrix GLCM. On the basis of studying various statistical characteristics of the * * generating matrix, Gotlieb and Kreyszig obtained four key characteristics of the gray * * generating matrix through experiments: energy, inertia, entropy and correlation. Another typical statistical method is to extract texture features from the autocorrelation function of the image (that is, the energy spectrum function of the image), that is, by calculating the energy spectrum function of the image, the thickness, directionality and other characteristic parameters of the texture are extracted.

2. Geometric method. The so-called geometric method is a texture feature analysis method based on the theory of texture primitives (basic texture elements) According to the theory of texture primitives, a complex texture can be formed by repeating several simple texture primitives in a certain regular form. Among geometric methods, there are two more influential algorithms: Voronio chessboard feature method and structure method.

3. Model method. The model method is based on the image construction model, and the parameters of the model are used as texture features. Typical methods include random field CRF model method, such as Markov random field (MRF) model method and Gibbs random field model method.

4. Signal processing method. Texture feature extraction and matching mainly include: gray * * * generation matrix, Tamura texture feature, autoregressive texture model, wavelet transform and so on. The feature extraction and matching of gray * * generating matrix mainly depend on four parameters: energy, inertia, entropy and correlation. Tamura texture features are based on the psychological research of human visual perception texture, and six attributes are proposed, namely: roughness, contrast, direction, line image, regularity and roughness. Autoregressive Texture Model (SAR) is an application example of Markov Random Field (MRF) model.