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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....
similarity ranking is one of the keys of a content-based image retrieval (CBIR) system. Among various methods, manifold ranking (MR) is popular for its application to relevance feedback in CBIR. Most existing MR methods only take the visual features into account in the similarity ranking, however, which is not accurate enough to reflect the intrinsic semantic structure of a given image database. In...
This paper presents a new relevance feedback scheme, which incorporates Extreme Learning Machine (ELM) to content-based image retrieval (CBIR) with relevance feedback. Relevance feedback schemes based on Support Vector Machine (SVM) have been proposed in previous paper. However, the performance of the schemes are often poor which is caused by the low speed of SVM algorithm in high dimension data....
Combining manifold ranking with active learning (MRAL for short) is one popular and successful technique for relevance feedback in content-based image retrieval (CBIR). Despite the success, conventional MRAL has two main drawbacks. First, the performance of manifold ranking is very sensitive to the scale parameter used for calculating the Laplacian matrix. Second, conventional MRAL does not take into...
Similarity measure is a critical component in image retrieval systems, and learning similarity measure from the relevance feedback has become a promising way to enhance retrieval performance. Existing approaches mainly focus on learning the visual similarity measure from online feedbacks or constructing the semantic similarity measure depended on historical feedbacks log. However, there is still a...
Support vector machine (SVM) based active learning technique plays a key role to alleviate the burden of labeling in relevance feedback. However, most SVM-based active learning algorithms are challenged by the small example problem and the asymmetric distribution problem. This paper proposes a novel active learning scheme that deals with SVM ensemble under the semi-supervised setting to address the...
One of the fundamental problems in content-based image retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. To narrow the gap, relevance feedback (RF) is introduced into CBIR. However, most RF methods are challenged by small size sample collection and asymmetric sample distributions between the positive and the negative samples. In this paper, a Bayesian...
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