High-definition image classification method based on kernel function and sparse coding

The invention discloses a high-definition image classification method based on a kernel function and sparse coding. The method comprises the following steps: extracting visual characteristics of each high-definition image; performing kernel function mapping on the visual characteristics, and converting an Euclidean space of the visual characteristics into a metric space; generating sparse codes of the type of the high-definition image according to the converted visual characteristics; and establishing a nonlinear image classifier according to the sparse codes of the type of the high-definition image, and determining the type to which the high-definition image belongs after performing weight endowing on each characteristic. According to the kernel function mapping of the visual characteristics, the influence of the related characteristics to the classification capacity is automatically improved according to the related endowed weights of the characteristics, the operation time of the classification process is reduced by utilizing the kernel method, the calculation amount is greatly reduced, and the classification efficiency is effectively improved, so that the classification method has high adaptability on sample space distribution of ahigh-definition image data set and has high robustness on a complex image.