Relevance Preserving Projection and Ranking Based on One-Class Classification for Web Image Search Reranking Image Search Reranking (ISR) technique aims at refining text-based search results by mining images’ visual content. Feature extraction and ranking function design are two key steps in ISR. Inspired by the idea of hypersphere in one-class classification, this paper proposes a feature extraction algorithm named Hypersphere-based Relevance Preserving Projection (HRPP) and a ranking function called Hypersphere-based Rank (H-Rank). Specifically, HRPP is a spectral embedding algorithm to transform original high-dimensional feature space into an intrinsically low-dimensional hypersphere space by preserving the manifold structure and relevance relationship among the images. H-Rank is a simple but effective ranking algorithm to sort the images by their distances to the hypersphere center. Moreover, to capture the user’s intent with minimum human interaction, a reversed KNN algorithm is proposed, which harvests enough pseudo-relevant images by requiring that the user gives only one click on the initially searched images. The HRPP method with reversed KNN is named as One-Click-based HRPP (OC-HRPP). Finally, OC-HRPP algorithm and H-Rank algorithm form a new ISR method, H-Reranking. Extensive experimental results on three large real-world datasets show that the proposed algorithms are effective. Moreover, the fact that only one relevant images are required to be labeled makes it has a strong practical significance.