Scene　recognition　aims　to　find　a　semantic　explanation　of　a　scene,　i.e.,　it　helps　intelligent　machines　to　know　where　they　are.　It　can　be　widely　applied　into　various　tasks　in　computer　vision　and　robotics.　Most　of　pioneer　methods　extracted　a　set　of　low-level　features　and　put　them　into　classifier　directly　to　identify　scene　category.　But　it　has　been　proved　that　low-level　features　do　not　work　well.　Currently　researchers　aim　to　overcome　the　semantic　gap　between　the　low-level　vision　features　and　high-level　semantic　categories　to　improve　the　recognition　performance.　Therefore,　much　attention　has　been　put　on　transforming　lowlevel　descriptors　into　richer　intermediate　representations.　This　paper　proposed　a　novel　method　based　on　intermediate　feature　representation　to　solve　the　problem　of　recognizing　the　semantic　category　of　scene　image.　This　proposed　method　uses　sparse　coding　on　SIFT　features　and　presents　a　spatial　layout　sensitive　pooling　method.　The　space　layout　for　pooling　is　based　on　three　rectangles　with　size　of　1∗1,1∗4　and　4∗1　in　each　image.　They　are　derived　from　inherent　characteristics　of　the　scene　images　by　regularly　dividing　the　image　in　horizontal　and　vertical　direction.　This　spatial　pooling　strategy　is　easier　and　it　can　get　optimal　representation　of　scene　images.　Extreme　learning　machine　(ELM)　is　used　as　a　classifier.　ELM　has　shown　great　ability　to　fit　nonlinear　classification　boundaries.　Experimental　results　have　shown　that　this　proposed　method　not　only　extracts　lower　dimension　image　feature　but　also　outperforms　other　similar　state-of-The-Art　methods　in　terms　of　recognition　performance.　©　2016　IEEE.