The invention provides a significance detection method based on sparse expression and label propagation. A new adjacent matrix is defined by use of the sparse expression, regions with common boundaries are called as adjacent regions and even data points in the same sub-space are defined as neighbors; then, a weight matrix is calculated through the similarity among regions in the image; parts of marginal regions are selected to be background labels; and label information of unlabeled regions is predicted through the weight matrix and the background labels obtained through the above method by applying a label propagation algorithm. The beneficial effects are that: by considering the relation between global information and local regions of the image, a new adjacent matrix is established; and by combining advantages of a sparse expression theory and the label propagation algorithm, the significance detection method has quite high accuracy and regression rate and is quite low in errors.