Biased support vector machine for relevance feedback in image retrieval

Hoi, Steven C. H.; Chan, Chi-Hang; Huang, Kaizhu; Lyu, Michael R.; King, Irwin

doi:10.1109/ijcnn.2004.1381186

Cited by 24 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SVM attempts to find the hyperplane that can achieve maximum separation between relevant and irrelevant images [17]. Biased-SVM and other methods overcome the limitations of standard SVM [18][19]. Another idea regarding relevance feedback is to create a subspace where the relevance images project closer together yet further away for the irrelevance images.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Medical Image Retrieval with Relevance Feedback via Pairwise Constraint Propagation

Wu¹,

Chen²,

Sun³

2014

KSII TIIS

View full text Add to dashboard Cite

Relevance feedback is an effective tool to bridge the gap between superficial image contents and medically-relevant sense in content-based medical image retrieval. In this paper, we propose an interactive medical image search framework based on pairwise constraint propagation. The basic idea is to obtain pairwise constraints from user feedback and propagate them to the entire image set to reconstruct the similarity matrix, and then rank medical images on this new manifold. In contrast to most of the algorithms that only concern manifold structure, the proposed method integrates pairwise constraint information in a feedback procedure and resolves the small sample size and the asymmetrical training typically in relevance feedback. We also introduce a long-term feedback strategy for our retrieval tasks. Experiments on two medical image datasets indicate the proposed approach can significantly improve the performance of medical image retrieval. The experiments also indicate that the proposed approach outperforms previous relevance feedback models.

show abstract

Section: Related Workmentioning

confidence: 99%

“…We have replaced the initial relation matrix Y with ' Y in algorithm 1 (18). Therefore, our retrieval system can benefit from both past feedback and current user feedback in this long-term learning strategy.…”

Section: Retrieval Framework With Long-term Feedbackmentioning

confidence: 99%