95,287 research outputs found
Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization
This paper tackles the problem of large-scale image-based localization (IBL)
where the spatial location of a query image is determined by finding out the
most similar reference images in a large database. For solving this problem, a
critical task is to learn discriminative image representation that captures
informative information relevant for localization. We propose a novel
representation learning method having higher location-discriminating power. It
provides the following contributions: 1) we represent a place (location) as a
set of exemplar images depicting the same landmarks and aim to maximize
similarities among intra-place images while minimizing similarities among
inter-place images; 2) we model a similarity measure as a probability
distribution on L_2-metric distances between intra-place and inter-place image
representations; 3) we propose a new Stochastic Attraction and Repulsion
Embedding (SARE) loss function minimizing the KL divergence between the learned
and the actual probability distributions; 4) we give theoretical comparisons
between SARE, triplet ranking and contrastive losses. It provides insights into
why SARE is better by analyzing gradients. Our SARE loss is easy to implement
and pluggable to any CNN. Experiments show that our proposed method improves
the localization performance on standard benchmarks by a large margin.
Demonstrating the broad applicability of our method, we obtained the third
place out of 209 teams in the 2018 Google Landmark Retrieval Challenge. Our
code and model are available at https://github.com/Liumouliu/deepIBL.Comment: ICC
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
Quantification and Comparison of Degree Distributions in Complex Networks
The degree distribution is an important characteristic of complex networks.
In many applications, quantification of degree distribution in the form of a
fixed-length feature vector is a necessary step. On the other hand, we often
need to compare the degree distribution of two given networks and extract the
amount of similarity between the two distributions. In this paper, we propose a
novel method for quantification of the degree distributions in complex
networks. Based on this quantification method,a new distance function is also
proposed for degree distributions, which captures the differences in the
overall structure of the two given distributions. The proposed method is able
to effectively compare networks even with different scales, and outperforms the
state of the art methods considerably, with respect to the accuracy of the
distance function
Feature Extraction from Degree Distribution for Comparison and Analysis of Complex Networks
The degree distribution is an important characteristic of complex networks.
In many data analysis applications, the networks should be represented as
fixed-length feature vectors and therefore the feature extraction from the
degree distribution is a necessary step. Moreover, many applications need a
similarity function for comparison of complex networks based on their degree
distributions. Such a similarity measure has many applications including
classification and clustering of network instances, evaluation of network
sampling methods, anomaly detection, and study of epidemic dynamics. The
existing methods are unable to effectively capture the similarity of degree
distributions, particularly when the corresponding networks have different
sizes. Based on our observations about the structure of the degree
distributions in networks over time, we propose a feature extraction and a
similarity function for the degree distributions in complex networks. We
propose to calculate the feature values based on the mean and standard
deviation of the node degrees in order to decrease the effect of the network
size on the extracted features. The proposed method is evaluated using
different artificial and real network datasets, and it outperforms the state of
the art methods with respect to the accuracy of the distance function and the
effectiveness of the extracted features.Comment: arXiv admin note: substantial text overlap with arXiv:1307.362
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