Robust visual reranking via sparsity and ranking constraints
Nobuyuki Morioka, Jingdong Wang · 2011
Visual reranking has become a widely-accepted method to improve traditional text-based image search engines. Its basic principle is that visually similar images should have similar ranking scores. While existing methods are different in specifics, almost all of them are based on explicit or implicit pseudo-relevance feedback (PRF). Explicit PRF-based approaches, including classification-based and clustering-based reranking, suffer from the difficulty of selecting reliable positive and negative samples. Implicit PRF-based approaches, such as graph-based and Bayesian visual reranking, deal with such unreliability by making use of the initial ranking in a soft manner, but have limited capability of promoting relevant images and lowering down irrelevant images.