A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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)
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Mean version space: a new active learning method for content-based image retrieval
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Adaptive image retrieval using a Graph model for semantic feature integration
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
A P2P strategy for QoS discovery and SLA negotiation in Grid environment
Future Generation Computer Systems
Content-based image retrieval with relevance feedback using random walks
Pattern Recognition
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In this paper, we propose a transductive learning method for content-based image retrieval: Multiple Random Walk (MRW). Its basic idea is to construct two generative models by means of Markov random walks, one for images relevant to the query concept and the other for the irrelevant ones. The goal is to obtain the likelihood functions of both classes. Firstly, MRW generates two random walks with virtual absorbing boundaries, and uses the absorbing probabilities as the initial estimation of the likelihood functions. Then it refines the two random walks through an EM-like iterative procedure in order to get more accurate estimation of the likelihood functions. Class priors are also obtained in this procedure. Finally, MRW ranks all the unlabeled images in the database according to their posterior probabilities of being relevant. By using both labeled and unlabeled data, MRW can be seen as a transductive learning method, which has been demonstrated to outperform inductive ones by previous research work. Systematic experiments on a general-purpose image database consisting of 5,000 Corel images demonstrate the superiority of MRW over state-of-the-art techniques.