Scene Categorization Through Using Objects Represented by Deep Features
Objects in scenes are thought to be important for scene recognition. In this paper, we propose to utilize scene specific objects represented by deep features for scene categorization. Our approach combines benefits of deep learning and Latent Support Vector Machine (LSVM) to train a set of scene specific object models for each scene category. Specifically, we first use deep Convolutional Neural Networks (CNNs) pre trained on the large scale object centric image database ImageNet to learn rich object features and a large number of general object concepts. Then, the pre trained CNNs is adopted to extract features from images in the target dataset, and initialize the learning of scene specific object models for each scene category. After initialization, the scene specific object models are obtained by alternating between searching over the most representative and discriminative regions of images in the target dataset and training linear SVM classifiers based on obtained region features. As a result, for each.
Int. J. Pattern Recognit. Artif. Intell.