A simple vector quantizer that combines low distortion with fast search and apply it to approximate nearest neighbor (ANN) search in high dimensional spaces. Leveraging the very same data structure that is used to provide non-exhaustive search, i.e. inverted lists or a multi-index, the idea is to locally optimize an individual product quantizer (PQ) per cell and use it to encode residuals. Local optimization is over rotation and space decomposition; interestingly, we apply a parametric solution that assumes a normal distribution and is extremely fast to train. With a reasonable space and time overhead that is constant in the data size, we set a new state-of-the-art on several public datasets, including a billion-scale one.

We exploit self-similaries, symmetries and repeating patterns to select features within a single image. We achieve the same performance compared to the full feature set with only a small fraction of its index size on a dataset of unique views of buildings or urban scenes, in the presence of one million distractors of similar nature. Our best solution is linear in the number of correspondences, with practical running times of just a few milliseconds.

A clustering method that combines the flexibility of Gaussian mixtures with the scaling properties needed to construct visual vocabularies for image retrieval. It is a variant of expectation-maximization that can converge rapidly while dynamically estimating the number of components. We employ approximate nearest neighbor search to speed-up the E-step and exploit its iterative nature to make search incremental, boosting both speed and precision.

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 a graffiti on a wall.

We present a new approach to image indexing and retrieval, which integrates appearance with global image geometry in the indexing process, while enjoying robustness against viewpoint change, photometric variations, occlusion, and background clutter. Each image is represented by a collection of feature maps and RANSAC-like matching is reduced to a number of set intersections. We extend min-wise independent permutations and finally exploit sparseness to build an inverted file whereby the retrieval process is sub-linear in the total number of images. We achieve excellent performance on 10^4 images, with a query time in the order of milliseconds.