This　paper　proposes　a　pre-attentive　visual　segmentation　method　by　using　Jeffrey　divergence　based　irregular　pyramid,　termed　as　＇JIP＇　algorithm.　The　proposed　JIP　algorithm　obtains　a　suboptimal　labeling　solution　under　the　condition　that　the　number　of　segments　is　not　manually　given.　This　algorithm　models　each　node　at　higher　pyramidal　levels　as　a　probabilistic　distribution,　based　on　which　Jeffrey　divergence　is　employed　to　measure　the　inter-node　distance　while　entropy　is　used　to　measure　the　intra-node　distance.　As　a　result,　the　neighborhood　system　for　the　graph　of　each　higher　pyramidal　level　can　be　automatically　set　up　according　to　the　similarity　and　graphic　constraints.　During　the　hierarchical　accumulation　of　the　irregular　pyramid,　the　segments　can　emerge　once　they　are　represented　by　single　nodes　at　certain　levels.　Experimental　results　have　shown　that　this　proposed　JIP　algorithm　outperforms　other　benchmark　segmentation　algorithms　in　terms　of　segmentation　accuracy　and　labeling　cost.　©　2013　IEEE.