Abstract The task of a visual landmark recognition system is to identify photographed buildings or objects in query photos and to provide the user with relevant information on them. With their increasing coverage of the world’s landmark buildings and objects, Internet photo collections are now being used as a source for building such systems in a fully automatic fashion. This process typically consists of three steps: clustering large amounts of images by the objects they depict; determining object names from user-provided tags; and building a robust, compact, and efficient recognition index. To this date, however, there is little empirical information on how well current approaches for those steps perform in a large-scale open-set mining and recognition task. Furthermore, there is little empirical information on how recognition performance varies for different types of landmark objects and where there is still potential for improvement. With this paper, we intend to fill these gaps. Using a dataset of 500 k images from Paris, we analyze each component of the landmark recognition pipeline in order to answer the following questions: How many and what kinds of objects can be discovered automatically? How can we best use the resulting image clusters to recognize the object in a query? How can the object be efficiently represented in memory for recognition? How reliably can semantic information be extracted? And finally: What are the limiting factors in the resulting pipeline from query to semantics? We evaluate how different choices of methods and parameters for the individual pipeline steps affect overall system performance and examine their effects for different query categories such as buildings, paintings or sculptures.
The recognition of a place depicted in an image typically adopts methods from image retrieval in large-scale databases. First, a query image is described as a “bag-of-features” and compared to every image in the database. Second, the most similar images are passed to a geometric verification stage. However, this is an inefficient approach when considering that some database images may be almost identical, and many image features may not repeatedly occur. We address this issue by clustering similar database images to represent distinct scenes, and tracking local features that are consistently detected to form a set of real-world landmarks. Query images are then matched to landmarks rather than features, and a probabilistic model of landmark properties is learned from the cluster to appropriately verify or reject putative feature matches. We present novelties in both a bag-of-features retrieval and geometric verification stage based on this concept. Results on a database of 200K images of popular tourist destinations show improvements in both recognition performance and efficiency compared to traditional image retrieval methods. State of the art data mining and image retrieval in community photo collections typically focus on popular subsets, e.g. images containing landmarks or associated to Wikipedia articles. We propose an image clustering scheme that, seen as vector quantization compresses a large corpus of images by grouping visually consistent ones while providing a guaranteed distortion bound. This allows us, for instance, to represent the visual content of all thousands of images depicting the Parthenon in just a few dozens of scene maps and still be able to retrieve any single, isolated, non-landmark image like a house or graffiti on a wall. Starting from a geo-tagged dataset, we first group images geographically and then visually, where each visual cluster is assumed to depict different views of the the same scene. We align all views to one reference image and construct a 2D scene map by preserving details from all images while discarding repeating visual features. Our indexing, retrieval and spatial matching scheme then operates directly on scene maps. We evaluate the precision of the proposed method on a challenging one-million urban image dataset. 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. There has been recent progress on the problem of recognizing specific objects in very large datasets. The most common approach has been based on the bag-of-words (BOW) method, in which local image features are clustered into visual words. This can provide significant savings in memory compared to storing and matching each feature independently. In this paper we take an additional step to reducing memory requirements by selecting only a small subset of the training features to use for recognition. This is based on the observation that many local features are unreliable or represent irrelevant clutter. We are able to select “useful” features, which are both robust and distinctive, by an unsupervised preprocessing step that identifies correctly matching features among the training images. We demonstrate that this selection approach allows an average of 4 of the original features per image to provide matching performance that is as accurate as the full set. In addition, we employ a graph to represent the matching relationships between images. Doing so enables us to effectively augment the feature set for each image through merging of useful features of neighboring images. We demonstrate adjacent and 2-adjacent augmentation, both of which give a substantial boost in performance.