We briefly group the related works for visual search reranking into two categories: recurrent pattern mining and multimodality fusion. The former assumes the existence of common patterns among relevant documents for reranking. The later predicts or learns the contribution of a modality in search reranking. The rapid development of Web 2.0 technologies has led to the surge of research activities in visual search. While visual documents are rich in audio-visual content and user-supplied texts, commercial visual search engines to date mostly perform retrieval by keyword matching. A common practice to improve search performance is to rerank the visual documents returned from a search engine using a larger and richer set of features. The ultimate goal is to seek consensus from various features for reordering the documents and boosting the retrieval precision. There are two general approaches along this direction: visual pattern mining and multi-modality fusion. The former mines the recurrent patterns, either explicitly or implicitly, from initial search results and then moves up the ranks of visually similar documents. Random walk, for instance, performs self-reranking through identifying documents with similar patterns based on inter-image similarity and initial rank scores. This category of approaches, nevertheless, seldom explores the joint utilization of multiple modalities. Instead, different modalities are treated independently. The research in this direction has proceeded along three different dimensions: self-reranking, crowd-reranking by exploiting online crowdsourcing knowledge, and example-based reranking by leveraging user-provided queries. Self-reranking seeks consensus from the initial ranked list as visual patterns for reranking. Employed probabilistic Latent Semantic Analysis (pLSA) for mining visual categories through clustering of images in the initial ranked list. Candidate images are then reranked based on the distance to the mined categories. Similar in spirit, employed information bottleneck (IB) reranking .