Probabilistic feature relevance learining for content-based image retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
A relevance feedback mechanism for content-based image retrieval
Information Processing and Management: an International Journal
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A novel log-based relevance feedback technique in content-based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
Relevance feedback (RF) is an iterative process which refines the retrievals by utilizing user's feedback marked on retrieved results Recent research has focused on the optimization for RF heuristic selection In this paper, we propose an automatic RF heuristic selection framework which automatically chooses the best RF heuristic for the given query The proposed method performs two learning tasks: query optimization and heuristic-selection optimization The particle swarm optimization (PSO) paradigm is applied to assist the learning tasks Experimental results tested on a content-based retrieval system with a real-world image database reveal that the proposed method outperforms several existing RF approaches using different techniques The convergence behavior of the proposed method is empirically analyzed.