Intensity Filtering and Group Fusion for Accurate Mobile Place Recognition

Mobile place recognition targets at matching query images captured by mobile devices with database images collected from vehicle-mounted cameras, such as Google street view panoramas, which plays an important role in many applications. However, current solutions deriving from image retrieval suffer from the problem of low precision on top results, which significantly challenges their usability. By investigating the state-of-the-art approaches, we find that the bad illumination significantly affects initial results, and these initial results are correlative in both spatial location and visual content, which can be utilized for further improvement. In this paper, we propose an effective approach to rerank initial top-ranked results to improve the recognition recall. First, initial retrieval results with low intensity are filtered as they usually depict irrelevant places with dark background. Second, the correlation between top-ranked results is modeled as a reciprocal neighborhood graph by jointly considering spatial location and visual similarity. With the graph, the initial results are reranked based on voting similarity from the query and reciprocal neighbors. In this way, the underlying structure of initial retrieval results is exploited for refining. Experimental results on the public Tokyo 24/7 and San Francisco landmark datasets demonstrate that the proposed approach can achieve persisting improvement of recognition recall over the state-of-the-art approach.