The application discloses a bridge
disease image texture feature extraction and classification method and
system, and particularly relates to the technical field of image
feature extraction and classification, and is used for solving the problem that the existing rectangular detection frame does not fit the physical boundary of the
disease, thereby causing non-stationary deviation of the
feature vector; the texture contrast is calculated pixel by pixel from each boundary to the inside in the
disease rectangular candidate frame, the demarcation line is determined according to the texture contrast change, and the core
disease area and the disease transition zone area are divided, the texture contrast difference sequence on both sides along the demarcation line is calculated point by point, and the texture virtuality is determined according to the local entropy of the difference sequence, the
texture feature vector of the transition zone area is weighted, the vector difference degree is calculated with the
texture feature vector of the core
disease area, when the difference degree exceeds the threshold value, the
texture feature vector of the core
disease area is corrected by weighting, the corrected texture
feature vector is input into a texture classifier, the
disease category confidence is corrected according to the
classification result, and the final
classification result is output.