Recent work on scene classification still makes use of generic CNN features
in a rudimentary manner. In this ICCV 2015 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
fine-tuning 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-the-art performance on two popular
scene benchmark datasets, MIT Indoor 67~\cite{MITIndoor67} and
Sun397~\cite{Sun397}

本研究介绍了一个基于深度 CNN 特征和区域提议技术的新型 pipeline，旨在从场景图像中提取判别性的视觉对象和局部区域进行分类，经过无监督和弱监督学习从大量高质量的 patches 中筛选出代表特定类别的判别性 objects 和 parts，并为相似 objects 和 parts 聚类形成 meta objects，该方法在两个流行的场景基准数据集上展现了领先水平。