Given the feature extraction of the furnace flame image produced in boilers and industrial production, a hierarchical adaptive method to extract salient points was proposed. First the Block Difference of Inverse Probabilities (BDIP) model was used to change the original image into BDIP image. On the basis of this, the BDIP image was made into Haar wavelet transform, the salient value of two-dimensional image was calculated by the improved weighted method, and then a non-equilibrium quadtree was built through the proposed adaptive method. The root of quadtree represented the salient value of the image, and the salient points number of subtree was determined according to the ratio of the salient value of every subtree to the salient value of parent node. The proposed extracting algorithm was salient points compared with the extracting algorithms based on BDIP and based on Haar wavelet transform. The experimental results show that edge accuracy and comprehensive feature retrieval accuracy at least increase by 10% and 3.5% respectively. The proposed method overcomes the shortcoming of traditional way that it extracts too many salient points and some extracted points are not salient, at the same time the method avoids local gather of salient points.