This paper introduces a simple yet effective method to improve visual word based image retrieval. Our method is based on an analysis of the k-reciprocal nearest neighbor structure in the image space. At query time the information obtained from this process is used to treat different parts of the ranked retrieval list with different distance measures. This leads effectively to a re-ranking of retrieved images. As we will show, this has two benefits: first, using different similarity measures for different parts of the ranked list allows for compensation of the “curse of dimensionality”. Second, it allows for dealing with the uneven distribution of images in the data space. Dealing with both challenges has very beneficial effect on retrieval accuracy. Furthermore, a major part of the process happens offline, so it does not affect speed at retrieval time. Finally, the method operates on the bag-of-words level only, thus it could be combined with any additional measures on e.g. either descriptor level or feature geometry making room for further improvement. We evaluate our approach on common object retrieval benchmarks and demonstrate a significant improvement over standard bag-of-words retrieval.

The state-of-the art in visual object retrieval from large databases allows to search millions of images on the object level. Recently, complementary works have proposed systems to crawl large object databases from community photo collections on the Internet. We combine these two lines of work to a large-scale system for auto-annotation of holiday snaps. The resulting method allows for automatic labeling objects such as landmark buildings, scenes, pieces of art etc. at the object level in a fully automatic manner. The labeling is multi-modal and consists of textual tags, geographic location, and related content on the Internet. Furthermore, the efficiency of the retrieval process is optimized by creating more compact and precise indices for visual vocabularies using background information obtained in the crawling stage of the system. We demonstrate the scalability and precision of the proposed method by conducting experiments on millions of images downloaded from community photo collections on the Internet.