Image matching, also refereed as feature point matching, is a fundamental issue in computer vision. In this paper, we propose to use local entropy based on Marr wavelets within scale-interaction to improve the accuracy of automatic feature detection in the context of image matching. The goal is to improve the accuracy of the feature matching step while exhibiting a highly representative set of features of the objects within both images. To improve reliability, we propose to exploit local entropy under a mesh division strategy in combination with a sensitive feature selection stage. Experimental results show that this algorithm can outperform some of the conventional feature extraction algorithms with higher subsequent matching recall rate of image matching.