International Journal of Computer Vision
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Image Information Systems: Where Do We Go From Here?
IEEE Transactions on Knowledge and Data Engineering
An effective region-based image retrieval framework
Proceedings of the tenth ACM international conference on Multimedia
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
An interactive semi-supervised approach for automatic image annotation
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Graph-based semi-supervised learning with multi-modality propagation for large-scale image datasets
Journal of Visual Communication and Image Representation
Editor's Choice Article: Sparse feature selection based on graph Laplacian for web image annotation
Image and Vision Computing
| Hi-index | 0.00 |
A novel keyword propagation method is proposed for image retrieval based on a recently developed manifold-ranking algorithm. In contrast to existing methods which train a binary classifier for each keyword, our keyword model is constructed in a straight forward manner by exploring the relationship among all images in the feature space in the learning stage. In relevance feedback, the feedback information can be naturally incorporated to refine the retrieval result by additional propagation processes. In order to speed up the convergence of the query concept, we adopt two active learning schemes to select images during relevance feedback. Furthermore, by means of keyword model update, the system can be self-improved constantly. The updating procedure can be performed online during relevance feedback without extra offline training. Systematic experiments on a general-purpose image database consisting of 5000 Corel images validate the effectiveness of the proposed method.