358 research outputs found
Context-Aware Embeddings for Automatic Art Analysis
Automatic art analysis aims to classify and retrieve artistic representations
from a collection of images by using computer vision and machine learning
techniques. In this work, we propose to enhance visual representations from
neural networks with contextual artistic information. Whereas visual
representations are able to capture information about the content and the style
of an artwork, our proposed context-aware embeddings additionally encode
relationships between different artistic attributes, such as author, school, or
historical period. We design two different approaches for using context in
automatic art analysis. In the first one, contextual data is obtained through a
multi-task learning model, in which several attributes are trained together to
find visual relationships between elements. In the second approach, context is
obtained through an art-specific knowledge graph, which encodes relationships
between artistic attributes. An exhaustive evaluation of both of our models in
several art analysis problems, such as author identification, type
classification, or cross-modal retrieval, show that performance is improved by
up to 7.3% in art classification and 37.24% in retrieval when context-aware
embeddings are used
An oil painters recognition method based on cluster multiple kernel learning algorithm
A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses on oil painter recognition and tries to find the mobile application to recognize the painter. This paper proposes a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: color, texture, and spatial layout, and generates multiple candidate kernels with different kernel functions. With the results of clustering numerous candidate kernels, we selected the sub-kernels with better classification performance, and use the traditional multiple kernel learning algorithm to carry out the multi-feature fusion classification. The algorithm achieves a better result on the Painting91 than using traditional multiple kernel learning directly
Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method
Image aesthetics assessment (IAA) is a challenging task due to its highly
subjective nature. Most of the current studies rely on large-scale datasets
(e.g., AVA and AADB) to learn a general model for all kinds of photography
images. However, little light has been shed on measuring the aesthetic quality
of artistic images, and the existing datasets only contain relatively few
artworks. Such a defect is a great obstacle to the aesthetic assessment of
artistic images. To fill the gap in the field of artistic image aesthetics
assessment (AIAA), we first introduce a large-scale AIAA dataset: Boldbrush
Artistic Image Dataset (BAID), which consists of 60,337 artistic images
covering various art forms, with more than 360,000 votes from online users. We
then propose a new method, SAAN (Style-specific Art Assessment Network), which
can effectively extract and utilize style-specific and generic aesthetic
information to evaluate artistic images. Experiments demonstrate that our
proposed approach outperforms existing IAA methods on the proposed BAID dataset
according to quantitative comparisons. We believe the proposed dataset and
method can serve as a foundation for future AIAA works and inspire more
research in this field. Dataset and code are available at:
https://github.com/Dreemurr-T/BAID.gitComment: Accepted by CVPR 202
- …