Approximate Nearest-Neighbour Search with Inverted Signature Slice Lists
نویسندگان
چکیده
In this paper we present an original approach for finding approximate nearest neighbours in collections of locality-sensitive hashes. The paper demonstrates that this approach makes high-performance nearest-neighbour searching feasible on Web-scale collections and commodity hardware with minimal degradation in search quality.
منابع مشابه
Predictive Indexing for Fast Search
We tackle the computational problem of query-conditioned search. Given a machine-learned scoring rule and a query distribution, we build a predictive index by precomputing lists of potential results sorted based on an expected score of the result over future queries. The predictive index datastructure supports an anytime algorithm for approximate retrieval of the top elements. The general appro...
متن کاملThe Area Code Tree for Approximate Nearest Neighbour Search in Dense Point Sets
In this paper, we present an evaluation of nearest neighbour searching using the Area Code tree. The Area Code tree is a trie-type structure that organizes area code representations of each point of interest (POI) in a data set. This data structure provides a fast method for locating an actual or approximate nearest neighbour POI for a query point. We first summarize the area code generation, i...
متن کاملRevisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors
This work addresses the problem of billion-scale nearest neighbor search. The state-of-the-art retrieval systems for billion-scale databases are currently based on the inverted multi-index[2], the recently proposed generalization of the inverted index structure. The multi-index provides a very fine-grained partition of the feature space that allows extracting concise and accurate short-lists of...
متن کاملRealtime Registration-Based Tracking via Approximate Nearest Neighbour Search
We introduce a new 2D visual tracking algorithm that utilizes an approximate nearest neighbour search to estimate per-frame state updates. We experimentally demonstrate that the new algorithm capable of estimating larger per-frame motions than the standard registration-based algorithms and that it is more robust in a vision-controlled robotic alignment task.
متن کاملMinimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours
We study the approximate nearest neighbour method for cost-sensitive classification on low-dimensional manifolds embedded within a high-dimensional feature space. We determine the minimax learning rates for distributions on a smooth manifold, in a cost-sensitive setting. This generalises a classic result of Audibert and Tsybakov. Building upon recent work of Chaudhuri and Dasgupta we prove that...
متن کامل