In　order　to　take　advantage　of　the　logical　structure　of　video　sequences　and　improve　the　recognition　accuracy　of　the　violence　videos,　a　novel　violent　scene　detection　method　based　on　3D　Histogram　of　Oriented　Gradients　(HOG3D)　and　Multi-Instance　Learning　(MIL)　is　proposed.　Firstly,　HOG3D　was　extracted　from　video　clips.　Then,　K-means　clustering　algorithm　was　implemented　to　generate　visual　words　vocabulary,　and　Bag　of　Visual　Words　(BoVW)　model　was　used　to　construct　the　final　feature　for　video　frame　sequence.　Next,　the　violent　scene　detection　issue　was　formulated　as　a　MIL　problem,　and　an　instance　clean　method　was　proposed　to　remove　noise　in　sample　data.　With　experimental　evaluations　on　the　well-known　Hockey　and　Movies　benchmarks,　the　results　demonstrate　that　Citation-kNN　is　more　suitable　than　the　other　two　tested　MIL　methods　named　Axis-Parallel　hyper　Rectangle　(APR)　and　mi-SVM　for　violent　scene　detection.　And　the　proposed　scheme　obtains　very　competitive　results:　92.7%　on　Hockey　and　98.8%　on　Movies　respectively　in　terms　of　mean　average　precision.　And　it　outperforms　the　state-of-the-art　on　Hockey　dataset　and　matches　the　best　known　accuracy　on　Movies,　achieving　the　best　balanced　accuracy　compared　with　all　other　methods.　©　2018,　Ubiquitous　International.　All　rights　reserved.