Automatic art analysis has been mostly focused on classifying artworks into different artistic styles. However, understanding an artistic representation involves more complex processes, such as identifying the elements in the scene or recognizing author influences. We present SemArt, a multi-modal dataset for semantic art understanding. SemArt is a collection of fine-art painting images in which each image is associated to a number of attributes and a textual artistic comment, such as those that appear in art catalogues or museum collections. To evaluate semantic art understanding, we envisage the Text2Art challenge, a multi-modal retrieval task where relevant paintings are retrieved according to an artistic text, and vice versa. We also propose several models for encoding visual and textual artistic representations into a common semantic space. Our best approach is able to find the correct image within the top 10 ranked images in the 45.5 of the test samples. Moreover, our models show remarkable levels of art understanding when compared against human evaluation.
This paper examines how far state-of-the-art machine vision algorithms can be used to retrieve common visual patterns shared by series of paintings. The research of such visual patterns, central to Art History Research, is challenging because of the diversity of similarity criteria that could relevantly demonstrate genealogical links. We design a methodology and a tool to annotate efficiently clusters of similar paintings and test various algorithms in a retrieval task. We show that pre-trained convolutional neural network can perform better for this task than other machine vision methods aimed at photograph analysis. We also show that retrieval performance can be significantly improved by fine-tuning a network specifically for this task. This paper offers a challenge for visual classification and content-based retrieval of artistic content. The challenge is posed from a museum-centric point of view offering a wide range of object types including paintings, photographs, ceramics, furniture, etc. The freely available dataset consists of 112,039 photographic reproductions of the artworks exhibited in the Rijksmuseum in Amsterdam, the Netherlands. We offer four automatic visual recognition challenges consisting of predicting the artist, type, material and creation year. We include a set of baseline results, and make available state-of-the-art image features encoded with the Fisher vector. Progress on this challenge improves the tools of a museum curator while improving content-based exploration by online visitors of the museum collection. The style of an image plays a significant role in how it is viewed, but style has received little attention in computer vision research. We describe an approach to predicting style of images, and perform a thorough evaluation of different image features for these tasks. We find that features learned in a multi-layer network generally perform best -- even when trained with object class (not style) labels. Our large-scale learning methods results in the best published performance on an existing dataset of aesthetic ratings and photographic style annotations. We present two novel datasets: 80K Flickr photographs annotated with 20 curated style labels, and 85K paintings annotated with 25 style genre labels. Our approach shows excellent classification performance on both datasets. We use the learned classifiers to extend traditional tag-based image search to consider stylistic constraints, and demonstrate cross-dataset understanding of style.    This paper aims to generate a better representation of visual arts, which plays a key role in visual arts analysis works. Museums and galleries have a large number of artworks in the database, hiring art experts to do analysis works (e.g., classification, annotation) is time consuming and expensive and the analytic results are not stable because the results highly depend on the experiences of art experts. The problem of generating better representation of visual arts is of great interests to us because of its application potentials and interesting research challenges---both content information and each unique style information within one artwork should be summarized and learned when generating the representation. For example, by studying a vast number of artworks, art experts summary and enhance the knowledge of unique characteristics of each visual arts to do visual arts analytic works, it is non-trivial for computer. In this paper, we present a unified framework, called DeepArt, to learn joint representations that can simultaneously capture contents and style of visual arts. This framework learns unique characteristics of visual arts directly from a large-scale visual arts dataset, it is more flexible and accurate than traditional handcraft approaches. We also introduce Art500k, a large-scale visual arts dataset containing over 500,000 artworks, which are annotated with detailed labels of artist, art movement, genre, etc. Extensive empirical studies and evaluations are reported based on our framework and Art500k and all those reports demonstrate the superiority of our framework and usefulness of Art500k. A practical system for visual arts retrieval and annotation is implemented based on our framework and dataset. Code, data and system are publicly available at http: deepart.ece.ust.hk. The objective of this work is to recognize object categories (such as animals and vehicles) in paintings, whilst learning these categories from natural images. This is a challenging problem given the substantial differences between paintings and natural images, and variations in depiction of objects in paintings. We first demonstrate that classifiers trained on natural images of an object category have quite some success in retrieving paintings containing that category. We then draw upon recent work in mid-level discriminative patches to develop a novel method for reranking paintings based on their spatial consistency with natural images of an object category. This method combines both class based and instance based retrieval in a single framework. We quantitatively evaluate the method over a number of classes from the PASCAL VOC dataset, and demonstrate significant improvements in rankings of the retrieved paintings over a variety of object categories.