Search reranking is considered as a best and common way to improves retrieval precision. However the problem is not trivial especially when we are considering multiple features or modalities for search in image and video retrieval. This paper suggests a new kind of reranking algorithm, the circular reranking, that supports the mutual exchange of information across multiple modalities for improving search performance, and follows the philosophy of strong performing modality could learn from

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... r ones, and weaker modality does benefit by interacting with stronger modalities. Technically, circular reranking conducts multiple cyclic runs of random walks by exchanging the ranking scores among different features. Contrasting the existing techniques, the reranking procedure reassures interaction between various modalities to seek consensus which are useful for reranking. In this paper, we study several circular reranking properties, including how and which order of information propagation should be configured to fully exploit the potential of modalities for reranking.