With　the　ever　growing　amount　of　digital　video　data　becoming　available,　people　are　gradually　challenged　to　come　up　with　methods　that　facilitate　video　indexing　and　retrieval.　This　paper　presents　a　key　frame　based　method　that　employs　shot　boundary　detection　and　＇bag-ofvisual-words　(BoW)＇　based　on　local　keypoints　for　key　frame　extraction　and　semantic　concept　detection.　The　performance　of　BoW　features　is　optimized　by　choosing　appropriate　representation　choices.　Once　video　frames　are　represented　by　BoW　features,　we　can　adopt　a　spectral　clustering　algorithm　for　the　generation　of　key　frames　in　each　shot,　and　then　we　can　classify　these　key　frames　using　support　vector　machines　for　video　indexing.　Finally,　this　paper　performs　a　query　by　concept　search　for　video　retrieval.　The　experimental　results　demonstrate　that　the　proposed　approach　is　capable　of　retrieving　videos.　Compared　with　the　existing　related　method,　the　proposed　method　yields　better　results　for　key　frame　extraction　and　yields　a　mean　average　precision　(MAP)　of　0.68　for　the　video　retrieval　model.　©　2018　Totem　Publisher,　Inc.　All　rights　reserved.