An Efficient Index for Visual Search in Appearance-based SLAM

Vector-quantization can be a computationally expensive step in visual bag-of-words (BoW) search when the vocabulary is large. A BoW-based appearance SLAM needs to tackle this problem for an efficient real-time operation. We propose an effective method to speed up the vector-quantization process in BoW-based visual SLAM. We employ a graph-based nearest neighbor search (GNNS) algorithm to this aim, and experimentally show that it can outperform the state-of-the-art. The graph-based search structure used in GNNS can efficiently be integrated into the BoW model and the SLAM framework. The graph-based index, which is a k-NN graph, is built over the vocabulary words and can be extracted from the BoW's vocabulary construction procedure, by adding one iteration to the k-means clustering, which adds small extra cost. Moreover, exploiting the fact that images acquired for appearance-based SLAM are sequential, GNNS search can be initiated judiciously which helps increase the speedup of the quantization process considerably

Search of low-dimensional magnetics on the basis of structural data: spin-1/2 antiferromagnetic zigzag chain compounds In2VO5, beta-Sr(VOAsO4)2,(NH4,K)2VOF4 and alpha-ZnV3O8

A new technique for searching low-dimensional compounds on the basis of structural data is presented. The sign and strength of all magnetic couplings at distances up to 12 A in five predicted new antiferromagnetic zigzag spin-1/2 chain compounds In2VO5, beta-Sr(VOAsO4)2, (NH4)2VOF4, K2VOF4 and alpha-ZnV3O8 were calculated. It was stated that in the compound In2VO5 zigzag spin chains are frustrated, since the ratio (J2/J1) of competing antiferromagnetic (AF) nearest- (J1) and AF next-to-nearest-neighbour (J2) couplings is equal to 1.68 that exceeds the Majumdar-Ghosh point by 1/2. In other compounds the zigzag spin chains are AF magnetically ordered single chains as value of ratios J2/J1 is close to zero. The interchain couplings were analyzed in detail.Comment: 14 pages, 6 figure, 1 table, minor change

Accelerating Nearest Neighbor Search on Manycore Systems

We develop methods for accelerating metric similarity search that are effective on modern hardware. Our algorithms factor into easily parallelizable components, making them simple to deploy and efficient on multicore CPUs and GPUs. Despite the simple structure of our algorithms, their search performance is provably sublinear in the size of the database, with a factor dependent only on its intrinsic dimensionality. We demonstrate that our methods provide substantial speedups on a range of datasets and hardware platforms. In particular, we present results on a 48-core server machine, on graphics hardware, and on a multicore desktop