A feature-based technique for separating specular and diffuse components of a single image is presented. In the proposed approach, Sharer's dichromatic reflection model is utilized, which assumed a light reflected from a surface point is linearly composed of diffuse and specular reflections. The major idea behind the proposed method is to classify the boundary pixels of the input image to be specular-related or diffuse-related. A fuzzy integral process is proposed to classify boundary pixels based on their local evidences, including specular and diffuse estimation information. Based on the classification result of boundary pixels, an integration method is evoked to reconstruct the specular and diffuse components of the input image, respectively. Unlike previous researches, the proposed method has no color segmentation and iterative operations. The experimental results have demonstrated that the proposed method can perform dichromatic reflectance separation effectively with small misadjustments and rapid convergence.