Interactive segmentation with Intelligent Scissors
Graphical Models and Image Processing
Feature normalization and likelihood-based similarity measures for image retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Multiple random walk and its application in content-based image retrieval
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A nearest-neighbor approach to relevance feedback in content based image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Query feedback for interactive image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Semi-Supervised learning using random walk limiting probabilities
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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In this paper, we propose a novel approach to content-based image retrieval with relevance feedback, which is based on the random walker algorithm introduced in the context of interactive image segmentation. The idea is to treat the relevant and non-relevant images labeled by the user at every feedback round as ''seed'' nodes for the random walker problem. The ranking score for each unlabeled image is computed as the probability that a random walker starting from that image will reach a relevant seed before encountering a non-relevant one. Our method is easy to implement, parameter-free and scales well to large datasets. Extensive experiments on different real datasets with several image similarity measures show the superiority of our method over different recent approaches.