Learning a weighted semantic manifold for content-based image retrieval

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.