Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information extraction. Existing defocus blur detection and segmentation methods have several limitations i.e., discriminating sharp smooth and blurred smooth regions, low recognition rate in noisy images, and high computational cost without having any prior knowledge of images i.e., blur degree and camera configuration. Hence, there exists a dire need to develop an effective method for defocus blur detection, and segmentation robust to the above-mentioned limitations. This paper presents a novel features descriptor local directional mean patterns (LDMP) for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur regions. We argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions, therefore, proposed LDMP features descriptor should reliably detect the defocus blurred regions. The fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the image. Additionally, the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy images. Experimental results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur detection. Evaluation and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.