The　complexity　of　physical　verification　increases　rapidly　with　fast　shrinking　technology　nodes.　Considering　only　design　rule　checking　(DRC)　constraints　or　lithography　models　cannot　capture　the　side　physical　effects　in　the　fabrication　process　well.　Thus,　it　is　desirable　to　consider　a　more　general　physical　verification　problem　with　various　types　of　hotspots.　In　this　paper,　we　apply　machine　learning　which　is　based　on　pixel-based　feature　extraction　to　deal　with　the　generalized　hotspot　detection　problem.　First,　a　two-dimensional　discrete　Fourier　transformation-based　pixel　extraction　method　is　proposed　to　alleviate　the　shifting　effect　and　produce　stable　hotspot　features.　Then,　a　pattern-based　layout　scanning　approach　is　developed　to　enhance　the　program　efficiency　while　preserving　good　detection　accuracy.　Finally,　we　design　two　false　alarm　reduction　strategies　to　effectively　reduce　the　number　of　detected　nonhotspots　and　further　improve　the　accuracy　of　hotspot　position.　Experimental　results　based　on　the　industrial　benchmarks　show　that　our　algorithm　outperforms　three　competitive　works　in　terms　of　accuracy,　false　alarm　rate,　efficiency,　and　time.