The invention belongs to the technical field of detection of image significance, which is characterized in that the significance target of any image can be detected, and relates to relevant knowledge of image processing. The method comprises the following steps of: firstly, over-segmenting an image into super pixels, and performing Harris interest point detection to form a convex hall; secondly, performing edge detection on the image and calculating the edge weight map of the image; thirdly, measuring color space information by using the edge weight image to obtain a prior image; fourthly, performing soft segmentation based on the prior image to obtain an observation likelihood probability; and lastly, combining the prior image with the observation likelihood probability by using a Bayesian framework to obtain a significance detection result. The method has the beneficial effects that background noise can be well eliminated, a high-brightness image target is smoothened, the situations of target color and background similarity, large targets and complex backgrounds which are always difficult to solve in significance detection can be handled, and the method can be well applied to ordinary images.