Integrating Spatial Information into Inverted Index for Large-Scale Image Retrieval

In recent years, large-scale image retrieval has been shown remarkable potential in real-life applications. To reduce retrieval time as searched database may contain thousands of images, Inverted Indexing is the basic technique, given images are represented by Bag-of-Words model. However, one major limitation of both standard Inverted Index and Bag-of-Words model is that they ignore spatial information of the visual words in images. This might reduce retrieval accuracy. In this paper, we introduce an approach to integrate spatial information into inverted index to improve accuracy while maintaining short retrieval time. Experiments conducted on several benchmark datasets (Oxford Building 5K, Paris 6K and Oxford Building 5K+100K) demonstrate the effectiveness of our proposed approach.