Video summarization approaches have various fields of application, specifically related to organizing, browsing and accessing large video databases. In this paper we propose and evaluate two novel approaches for video summarization, one based on spectral methods and the other on ant-tree clustering. The overall summary creation process is broke down in two steps: detection of similar scenes and extraction of the most representative ones. While clustering approaches are used for scene segmentation, the post-processing logic merges video scenes into a subset of user relevant scenes. In the case of the spectral approach, representative scenes are extracted following the logic that important parts of the video are related with high motion activity of segments within scenes. In the alternative approach we estimate a subset of relevant video scene using ant-tree optimization approaches and in a supervised scenario certain scenes of no interest to the user are recognized and excluded from the summary. An experimental evaluation validating the feasibility and the robustness of these approaches is presented.