254 research outputs found
Capturing Topology in Graph Pattern Matching
Graph pattern matching is often defined in terms of subgraph isomorphism, an
NP-complete problem. To lower its complexity, various extensions of graph
simulation have been considered instead. These extensions allow pattern
matching to be conducted in cubic-time. However, they fall short of capturing
the topology of data graphs, i.e., graphs may have a structure drastically
different from pattern graphs they match, and the matches found are often too
large to understand and analyze. To rectify these problems, this paper proposes
a notion of strong simulation, a revision of graph simulation, for graph
pattern matching. (1) We identify a set of criteria for preserving the topology
of graphs matched. We show that strong simulation preserves the topology of
data graphs and finds a bounded number of matches. (2) We show that strong
simulation retains the same complexity as earlier extensions of simulation, by
providing a cubic-time algorithm for computing strong simulation. (3) We
present the locality property of strong simulation, which allows us to
effectively conduct pattern matching on distributed graphs. (4) We
experimentally verify the effectiveness and efficiency of these algorithms,
using real-life data and synthetic data.Comment: VLDB201
Three IndicesCalculationof Certain Crown Molecular Graphs
As molecular graph invariant topological indices, harmonic index, zeroth-order general Randic index and Co-PI index have been studied in recent years for prediction of chemicalphenomena. In this paper, we determine the harmonic index, zeroth-order general Randic index andCo-PI indexof certain r-crown molecular graphs
Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements
Background subtraction has been a fundamental and widely studied task in
video analysis, with a wide range of applications in video surveillance,
teleconferencing and 3D modeling. Recently, motivated by compressive imaging,
background subtraction from compressive measurements (BSCM) is becoming an
active research task in video surveillance. In this paper, we propose a novel
tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames
into backgrounds with spatial-temporal correlations and foregrounds with
spatio-temporal continuity in a tensor framework. In this approach, we use 3D
total variation (TV) to enhance the spatio-temporal continuity of foregrounds,
and Tucker decomposition to model the spatio-temporal correlations of video
background. Based on this idea, we design a basic tensor RPCA model over the
video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize
the correlations among the groups of similar 3D patches of video background, we
further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint
tensor Tucker decompositions of 3D patch groups for modeling the video
background. Efficient algorithms using alternating direction method of
multipliers (ADMM) are developed to solve the proposed models. Extensive
experiments on simulated and real-world videos demonstrate the superiority of
the proposed approaches over the existing state-of-the-art approaches.Comment: To appear in IEEE TI
Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal Action Localization
Weakly-supervised temporal action localization aims to localize and recognize
actions in untrimmed videos with only video-level category labels during
training. Without instance-level annotations, most existing methods follow the
Segment-based Multiple Instance Learning (S-MIL) framework, where the
predictions of segments are supervised by the labels of videos. However, the
objective for acquiring segment-level scores during training is not consistent
with the target for acquiring proposal-level scores during testing, leading to
suboptimal results. To deal with this problem, we propose a novel
Proposal-based Multiple Instance Learning (P-MIL) framework that directly
classifies the candidate proposals in both the training and testing stages,
which includes three key designs: 1) a surrounding contrastive feature
extraction module to suppress the discriminative short proposals by considering
the surrounding contrastive information, 2) a proposal completeness evaluation
module to inhibit the low-quality proposals with the guidance of the
completeness pseudo labels, and 3) an instance-level rank consistency loss to
achieve robust detection by leveraging the complementarity of RGB and FLOW
modalities. Extensive experimental results on two challenging benchmarks
including THUMOS14 and ActivityNet demonstrate the superior performance of our
method.Comment: Accepted by CVPR 2023. Code is available at
https://github.com/RenHuan1999/CVPR2023_P-MI
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