Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Performance measures for multilabel evaluation: a case study in the area of image classification
Proceedings of the international conference on Multimedia information retrieval
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
The University of Aamsterdam's concept detection system at ImageCLEF 2009
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Graph-based methods for the automatic annotation and retrieval of art prints
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
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Artistic image understanding is an interdisciplinary research field of increasing importance for the computer vision and the art history communities. For computer vision scientists, this problem offers challenges where new techniques can be developed; and for the art history community new automatic art analysis tools can be developed. On the positive side, artistic images are generally constrained by compositional rules and artistic themes. However, the low-level texture and color features exploited for photographic image analysis are not as effective because of inconsistent color and texture patterns describing the visual classes in artistic images. In this work, we present a new database of monochromatic artistic images containing 988 images with a global semantic annotation, a local compositional annotation, and a pose annotation of human subjects and animal types. In total, 75 visual classes are annotated, from which 27 are related to the theme of the art image, and 48 are visual classes that can be localized in the image with bounding boxes. Out of these 48 classes, 40 have pose annotation, with 37 denoting human subjects and 3 representing animal types. We also provide a complete evaluation of several algorithms recently proposed for image annotation and retrieval. We then present an algorithm achieving remarkable performance over the most successful algorithm hitherto proposed for this problem. Our main goal with this paper is to make this database, the evaluation process, and the benchmark results available for the computer vision community.