The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Link analysis ranking: algorithms, theory, and experiments
ACM Transactions on Internet Technology (TOIT)
Learning structured prediction models: a large margin approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Correlated Label Propagation with Application to Multi-label Learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Imagerank: spectral techniques for structural analysis of image database
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - 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
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image annotation via graph learning
Pattern Recognition
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Link analysis, eigenvectors and stability
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Recovering the past through computation: new techniques for cultural heritage
Proceedings of the international conference on Multimedia information retrieval
Performance measures for multilabel evaluation: a case study in the area of image classification
Proceedings of the international conference on Multimedia information retrieval
Image annotation with tagprop on the MIRFLICKR set
Proceedings of the international conference on Multimedia information retrieval
Structured max-margin learning for multi-label image annotation
Proceedings of the ACM International Conference on Image and Video Retrieval
Detection of forgery in paintings using supervised learning
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
Labelset anchored subspace ensemble (LASE) for multi-label annotation
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Artistic image classification: an analysis on the PRINTART database
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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The analysis of images taken from cultural heritage artifacts is an emerging area of research in the field of information retrieval. Current methodologies are focused on the analysis of digital images of paintings for the tasks of forgery detection and style recognition. In this paper, we introduce a graph-based method for the automatic annotation and retrieval of digital images of art prints. Such method can help art historians analyze printed art works using an annotated database of digital images of art prints. The main challenge lies in the fact that art prints generally have limited visual information. The results show that our approach produces better results in a weakly annotated database of art prints in terms of annotation and retrieval performance compared to state-of-the-art approaches based on bag of visual words.