IEEE Trans Pattern Anal Mach Intell - Trinary-Projection Trees for Approximate Nearest Neighbor Search.

Tópicos

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Resumo

In this paper, we address the problem of approximate nearest neighbor (ANN) search for visual descriptor indexing. Most spatial partition trees, such as KD trees, VP trees and so on, follow the hierarchical binary space partitioning framework. The key effort is to design different partition functions (hyperplane or hypersphere) to divide the points so that (1) the data points can be well grouped to support effective NN candidate location and (2) the partition functions can be quickly evaluated to support efficient NN candidate location. We design a trinary-projection-direction-based partition function. The trinary-projection direction is defined as a combination of a few coordinate axes with the weights being $1$ or $-1$. We pursue the projection direction using the widely-adopted maximum variance criterion to guarantee good space partitioning and find fewer coordinate axes to guarantee efficient partition function evaluation. We present a coordinate-wise enumeration algorithm to find the principal trinary-projection direction. In addition, we give an extension using multiple randomized trees for improved performance. We justify our approach on large scale local patch indexing and similar image search.

Resumo Limpo

paper address problem approxim nearest neighbor ann search visual descriptor index spatial partit tree kd tree vp tree follow hierarch binari space partit framework key effort design differ partit function hyperplan hyperspher divid point data point can well group support effect nn candid locat partit function can quick evalu support effici nn candid locat design trinaryprojectiondirectionbas partit function trinaryproject direct defin combin coordin axe weight pursu project direct use widelyadopt maximum varianc criterion guarante good space partit find fewer coordin axe guarante effici partit function evalu present coordinatewis enumer algorithm find princip trinaryproject direct addit give extens use multipl random tree improv perform justifi approach larg scale local patch index similar imag search

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