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Architectural structures, the basic elements of the urban web, are an aggregation of buildings that have been built at different times, with different materials, and in different styles. Through research, they can be divided into groups that present common morphological attributes and refer to different historical periods with particular social, economic, and cultural characteristics. The identification of these common repeating elements and organizational construction structures leads to the identification of the "type" of the building, which until now has required specialized knowledge, time, and customized proof checking. Recent developments in the field of artificial intelligence (AI) and, more specifically, in deep learning (DL) appear to contribute gradually to the study of the typological evolution of buildings, especially those of cultural heritage (CH). In this paper, we present a deep-learning-based method for the classification of modern Athenian architecture (since 1830) using the YOLO algorithm. This research work can contribute to the digital management of the existing urban building stock, the autonomous large-scale categorization of data that are available from street view images, and the enhancement of the tangible CH. [ABSTRACT FROM AUTHOR]

The performance of computer vision algorithms can severely degrade in the presence of a variety of distortions. While image enhancement algorithms have evolved to optimize image quality as measured according to human visual perception, their relevance in maximizing the success of computer vision algorithms operating on the enhanced image has been much less investigated. We consider the problem of image enhancement to combat Gaussian noise and low resolution with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising or super resolution, allowing for optimization of the enhancement CNN to maximize retrieval performance. This framework allows us to couple enhancement to the retrieval problem. We also consider the problem of adapting image features for robust retrieval performance in the presence of distortions. We show through experiments on distorted images of the Oxford and Paris buildings datasets that our algorithms yield improved mean average precision when compared to using enhancement methods that are oblivious to the task of image retrieval. 1 1 The material in this paper appeared in part at the 2018 IEEE International Conference on Image Processing, Athens, Greece. • Image enhancement for robust image retrieval (IR) in the presence of distortions • Quality assessment for image retrieval (QAIR) convolutional neural network (CNN) predicts image retrieval performance of a distorted image in terms of average precision • An enhancement CNN is optimized for image retrieval using the QAIR CNN • Experiments with noise and low resolution on multiple datasets reveal performance improvements • Method to adapt deep learning features for IR that are robust to distortions [ABSTRACT FROM AUTHOR]

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