相关论文: Recognizing Characters in Art History Using Deep L…

Deep learning has paved the way for strong recognition systems which are often both trained on and applied to natural images. In this paper, we examine the give-and-take relationship between such visual recognition systems and the rich…

Recommendation systems based on image recognition could prove a vital tool in enhancing the experience of museum audiences. However, for practical systems utilizing wearable cameras, a number of challenges exist which affect the quality of…

The artistic style of a painting is a subtle aesthetic judgment used by art historians for grouping and classifying artwork. The recently introduced `neural-style' algorithm substantially succeeds in merging the perceived artistic style of…

The massive digitization of artworks during the last decades created the need for categorization, analysis, and management of huge amounts of data related to abstract concepts, highlighting a challenging problem in the field of computer…

Identifying sitters in historical paintings is a key task for art historians, offering insight into their lives and how they chose to be seen. However, the process is often subjective and limited by the lack of data and stylistic…

Considering the existence of very large amount of available data repositories and reach to the very advanced system of hardware, systems meant for facial identification ave evolved enormously over the past few decades. Sketch recognition is…

Main characters in images are the most important humans that catch the viewer's attention upon first look, and they are emphasized by properties such as size, position, color saturation, and sharpness of focus. Identifying the main…

Despite recent advances in object detection using deep learning neural networks, these neural networks still struggle to identify objects in art images such as paintings and drawings. This challenge is known as the cross depiction problem…

As one of the fundamental problems in document analysis, scene character recognition has attracted considerable interests in recent years. But the problem is still considered to be extremely challenging due to many uncontrollable factors…

In this paper, we report on our efforts for using Deep Learning for classifying artifacts and their features in digital visuals as a part of the Neoclassica framework. It was conceived to provide scholars with new methods for analyzing and…

Understanding how cities visually differ from each others is interesting for planners, residents, and historians. We investigate the interpretation of deep features learned by convolutional neural networks (CNNs) for city recognition. Given…

With the increasing availability of large digitized fine art collections, automated analysis and classification of paintings is becoming an interesting area of research. However, due to domain specificity, implicit subjectivity, and…

We address the problem of recognizing situations in images. Given an image, the task is to predict the most salient verb (action), and fill its semantic roles such as who is performing the action, what is the source and target of the…

This paper addresses the interpretability of deep learning-enabled image recognition processes in computer vision science in relation to theories in art history and cognitive psychology on the vision-related perceptual capabilities of…

Previous work has shown that the artist of an artwork can be identified by use of computational methods that analyse digital images. However, the digitised artworks are often investigated at a coarse scale discarding many of the important…

Art Media Classification problem is a current research area that has attracted attention due to the complex extraction and analysis of features of high-value art pieces. The perception of the attributes can not be subjective, as humans…

Deep neural networks have become increasingly successful at solving classic perception problems such as object recognition, semantic segmentation, and scene understanding, often reaching or surpassing human-level accuracy. This success is…

We propose ArtSAGENet, a novel multimodal architecture that integrates Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs), to jointly learn visual and semantic-based artistic representations. First, we illustrate the…

In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential in- formation about the spatial topology and geometric details of faces while…

In the recent time deep learning has achieved huge popularity due to its performance in various machine learning algorithms. Deep learning as hierarchical or structured learning attempts to model high level abstractions in data by using a…