Froth　image　segmentation　is　an　important　and　basic　part　in　an　online　froth　monitoring　system　in　mineral　processing.　The　fast　and　accurate　bubble　delineation　in　a　froth　image　is　significant　for　the　subsequent　froth　surface　characterization.　This　paper　proposes　a　froth　image　segmentation　method　combining　image　classification　and　image　segmentation.　In　the　method,　an　improved　Harris　corner　detection　algorithm　is　applied　to　classify　froth　images　first.　Then,　for　each　class,　the　images　are　segmented　by　automatically　choosing　the　corresponding　parameters　for　identifying　bubble　edge　points　through　extracting　the　local　gray　value　minima.　Finally,　on　the　basis　of　the　edge　points,　the　bubbles　are　delineated　by　using　a　number　of　post-processing　functions.　Compared　with　the　widely　used　Watershed　algorithm　and　others　for　a　number　of　lead　zinc　froth　images　in　a　flotation　plant,　the　new　method　(algorithm)　can　alleviate　the　over-segmentation　problem　effectively.　The　experimental　results　show　that　the　new　method　can　produce　good　bubble　delineation　results　automatically.　In　addition,　its　processing　speed　can　also　meet　the　online　measurement　requirements.