VLAD re-ranking: Iteratively estimating the probability of relevance with relationships between dataset images

• Our method is an efficient framework which combines BOW and VLAD for better accuracy.
• We regard the problem of similarity evaluation as probability evaluation.
• Probability estimation is formulated with the application of the image graph.
• Our iterative evaluation procedure further improves the retrieval accuracy.

Vector of Locally Aggregated Descriptor (VLAD) and Bag-of-word (BOW) are two typical frameworks for specific object retrieval. Many enhancements of baseline BOW approach combine with the spatial information of local features, and can significantly improve the retrieval accuracy via spatial-information-based re-ranking approaches. The VLAD approach is designed as a method with high efficiency in terms of time and memory consumption, which accumulates local features as a compacted low-dimensional descriptor for image representation. Due to the fundamental difference between the two frameworks, methods for improving the accuracy of BOW cannot generally be applied for VLAD. In this paper, we introduce a retrieval framework that aims to combine the advantages of both VLAD and BOW. In the off-line part, image representations of both BOW and VLAD are generated, and we further use BOW to construct an image graph, which is a structure containing relationships between the dataset images. For real-time retrieval, we first follow the typical VLAD framework to obtain initial retrieval results. Then, an iterative re-ranking method is proposed to re-estimate the probabilities of relevance with the support of the image graph, and the final ranks of the images are obtained according to the values of probability values. The experimental results show that, compared to state-of-the-art VLAD methods, our framework achieves better retrieval accuracy with almost no compromise in time and memory efficiency.