Recent work on scene classification still makes use of generic CNN features in a rudimentary manner. In this paper, we present a novel pipeline built upon deep CNN features to harvest discriminative visual objects and parts for scene classification. We first use a region proposal technique to generate a set of high-quality patches potentially containing objects, and apply a pre-trained CNN to extract generic deep features from these patches. Then we perform both unsupervised and weakly supervised learning to screen these patches and discover discriminative ones representing category-specific objects and parts. We further apply discriminative clustering enhanced with local CNN finetuning to aggregate similar objects and parts into groups, called meta objects. A scene image representation is constructed by pooling the feature response maps of all the learned meta objects at multiple spatial scales. We have confirmed that the scene image representation obtained using this new pipeline is capable of delivering state-of-theart performance on two popular scene benchmark datasets, MIT Indoor 67 [22] and Sun397 [31] .
REF first used a region proposal technique to generate a set of high-quality patches, potentially containing objects, applied pretrained CNNs to extract generic deep features from these patches, and finally performed both unsupervised and weakly supervised learning to screen these patches and discover discriminative patches that represent category-specific objects and parts.
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification
2015 IEEE International Conference on Computer Vision (ICCV)
2015 IEEE International Conference on Computer Vision (ICCV)
Computer Science