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Large scale visual surveillance generates huge volumes of data at a rapid pace, giving rise to massive image repositories. Efficient and reliable access to relevant data in these ever growing databases is a highly challenging task due to the complex nature of surveillance objects. Furthermore, inter-class visual similarity between vehicles requires extraction of fine-grained and highly discriminative features. In recent years, features from deep convolutional neural networks (CNN) have exhibited state-of-the-art performance in image retrieval. However, these features have been used without regard to their sensitivity to objects of a particular class. In this paper, we propose an object-oriented feature selection mechanism for deep convolutional features from a pre-trained CNN. Convolutional feature maps from a deep layer are selected based on the analysis of their responses to surveillance objects. The selected …