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Recent work on scene classification still makes use of generic CNN featuresin a rudimentary manner. In this ICCV 2015 paper, we present a novel pipelinebuilt upon deep CNN features to harvest discriminative visual objects and partsfor scene classification. We first use a region proposal technique to generatea set of high-quality patches potentially containing objects, and apply apre-trained CNN to extract generic deep features from these patches. Then weperform both unsupervised and weakly supervised learning to screen thesepatches and discover discriminative ones representing category-specific objectsand parts. We further apply discriminative clustering enhanced with local CNNfine-tuning to aggregate similar objects and parts into groups, called metaobjects. A scene image representation is constructed by pooling the featureresponse maps of all the learned meta objects at multiple spatial scales. Wehave confirmed that the scene image representation obtained using this newpipeline is capable of delivering state-of-the-art performance on two popularscene benchmark datasets, MIT Indoor 67~\cite{MITIndoor67} andSun397~\cite{Sun397}

Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification