Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Adaptive relevance feedback based on Bayesian inference for image retrieval
Signal Processing - Special section on content-based image and video retrieval
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Top 10 algorithms in data mining
Knowledge and Information Systems
BALAS: Empirical Bayesian learning in the relevance feedback for image retrieval
Image and Vision Computing
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Bayesian learning (BL) based relevance feedback (RF) schemes plays a key role for boosting image retrieval performance. However, traditional BL based RF schemes are often challenged by the small example problem and asymmetrical training example problem. This paper presents a novel scheme that embeds the query point movement (QPM) technique into the Bayesian framework for improving RF performance. In particular, we use an asymmetric learning methodology to determine the parameters of Bayesian learner, thus termed as asymmetric Bayesian learning. For one thing, QPM is applied to estimate the distribution of the relevant class by exploiting labeled positive and negative examples. For another, a semi-supervised learning mechanism is used to tackle the scarcity of negative examples. Concretely, a random subset of the unlabeled images is selected as the candidate negative examples, of which the problematic data are then eliminated by using QPM. Then, the cleaned unlabeled images are regarded as additional negative examples which are helpful to estimate the distribution of the irrelevant class. Experimental results show that the proposed scheme is more effective than some existing approaches.