Convolutional　neural　network　(CNN)-based　approaches　have　received　state-of-the-art　results　in　scene　classification.　Features　from　the　output　of　fully　connected　(FC)　layers　express　one-dimensional　semantic　information　but　lose　the　detailed　information　of　objects　and　the　spatial　information　of　scene　categories.　On　the　contrary,　deep　convolutional　features　have　been　proved　to　be　more　suitable　for　describing　an　object　itself　and　the　spatial　relations　among　objects　in　an　image.　In　addition,　the　feature　map　from　each　layer　is　max-pooled　within　local　neighborhoods,　which　weakens　the　invariance　of　global　consistency　and　is　unfavorable　to　scenes　with　highly　complicated　variation.　To　cope　with　the　above　issues,　an　orderless　multi-channel　mid-level　image　representation　on　pre-trained　CNN　features　is　proposed　to　improve　the　classification　performance.　The　mid-level　image　representation　of　two　channels　from　the　FC　layer　and　the　deep　convolutional　layer　are　integrated　at　multi-scale　levels.　A　sum　pooling　approach　is　also　employed　to　aggregate　multi-scale　mid-level　image　representation　to　highlight　the　importance　of　the　descriptors　beneficial　for　scene　classification.　Extensive　experiments　on　SUN397　and　MIT　67　indoor　datasets　demonstrate　that　the　proposed　method　achieves　promising　classification　performance.　(C)　2017　SPIE　and　IS&T