Video skimming is one of the recently, getting popular technique for preparing preview for long watching video sequences. Most of the video skimming techniques developed in the literature uses manual intervention of users to prepare the review. Mostly the literature reported video skimming for sports and movie industries. In sports the portion of video where audience claps are used and in movie important contents are manually selected for preparing the preview. However in literature rarely any work reported for skimming of lecture video sequences. Lecture videos are generally, recorded indoor, low illuminated, noisy environment condition and contents of the scene rarely changes much. Hence designing an automatic skimming scheme is quite difficult task. In this article, we put forward an intelligent expert video skimming technique for lecture video sequences, where human intervention is not required. In the proposed scheme, initially the lecture video is segmented into a number of shots. We proposed the use of radiometric correlation technique for lecture video segmentation or finding the shot transitions. After getting the shot transitions in a video, the shots are recognized. The fuzzy K-nearest neighborhood technique is proposed to recognize the shots in a video. The shots are recognized into three categories: title slides, written texts/displayed slides and talking heads/writing hands. Three contrast based features: one existing i.e., average sharpness (AS) and two newly proposed: relative height (RH) and edge potential (EP) are used to find the contents of a frame. The frames with different contrast values are categorized to prepare the video skimming or the capsule. The media recreation is achieved by selecting a set of frames around these selected content frames. The effectiveness of the proposed scheme is demonstrated in this paper using five test sequences, including three NPTEL and two non NPTEL. It is also observed that the capsule prepared by the proposed scheme, provides a better preview of the actual sequence. The performance of the proposed scheme is tested by comparing it against three state-of-the-art techniques. The evaluation of the proposed scheme is carried out by using three evaluation measures. It is also observed that the proposed scheme is found to be better than that of the existing schemes. © 2020 Elsevier Ltd