We propose a novel weighted manifold-ranking based image retrieval method to improve the effectiveness of traditional manifold methods. Specifically, we apply the SVM-based relevance feedback technique to create semantic clusters for computing the reliability score of each database image. We then incorporate the reliability scores into the affinity matrix to construct a weighted manifold structure. We finally create an asymmetric relevance vector to store users' positively and negatively labeled information. Our system ensures to propagate the labels in the relevance vector to the images with high reliability scores and discriminately spread the ranking scores of positive and negative images via the weighted manifold structure.Extensive experiments demonstrate our system outperforms the other manifold systems and SVM-based systems in the context of both correct and erroneous feedback.

We propose to combine short-term block-based fuzzy support vector machine (FSVM) learning and long-term dynamic semantic clustering (DSC) learning to bridge the semantic gap in content-based image retrieval. The short-term learning addresses the small sample problem by incorporating additional image blocks to enlarge the training set. Specifically, it applies the nearest neighbor mechanism to choose additional similar blocks. A fuzzy metric is computed to measure the fidelity of the actual class information of the additional blocks. The FSVM is finally applied on the enlarged training set to learn a more accurate decision boundary for classifying images. The long-term learning addresses the large storage problem by building dynamic semantic clusters to remember the semantics learned during all query sessions. Specifically, it applies a cluster-image weighting algorithm to find the images most semantically related to the query. It then applies a DSC technique to adaptively learn and update the semantic categories. Our extensive experimental results demonstrate that the proposed short-term, long-term, and collaborative learning methods outperform their peer methods when the erroneous feedback resulting from the inherent subjectivity of judging relevance, user laziness, or maliciousness is involved. The collaborative learning system achieves better retrieval precision and requires significantly less storage space than its peers. C 2011 Wiley Periodicals, Inc.

We propose a novel weighted semantic manifold ranking system for content-based image retrieval. This manifold builds a more accurate intrinsic structure for the proper image space by combining visual and semantic relevance relations. Specifically, we apply the learning mechanism to capture users' semantic concepts in clusters and extract high-level semantic features for each database image. We then incorporate the reliability score, the fuzzy membership, and the composite low-level and high-level relation into the traditional affinity matrix to construct a weighted semantic manifold structure. We finally create an asymmetric relevance vector to propagate positive and negative labels via the proposed manifold structure to images with high similarities. Extensive experiments demonstrate our system outperforms other manifold systems and learning systems in the context of both correct and erroneous feedback.

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This paper presents an inter-query semantic learning approach for image retrieval with relevance feedback. The proposed system combines the kernel biased discriminant analysis (KBDA) based low-level learning and semantic log file (SLF) based high-level learning to achieve high retrieval accuracy after the first iteration. User's relevance feedback is utilized for updating both low-level and highlevel features of the query image. Extensive experiments demonstrate our system outperforms three peer systems.