3,455 research outputs found
DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition
Being symmetric positive-definite (SPD), covariance matrix has traditionally
been used to represent a set of local descriptors in visual recognition. Recent
study shows that kernel matrix can give considerably better representation by
modelling the nonlinearity in the local descriptor set. Nevertheless, neither
the descriptors nor the kernel matrix is deeply learned. Worse, they are
considered separately, hindering the pursuit of an optimal SPD representation.
This work proposes a deep network that jointly learns local descriptors,
kernel-matrix-based SPD representation, and the classifier via an end-to-end
training process. We derive the derivatives for the mapping from a local
descriptor set to the SPD representation to carry out backpropagation. Also, we
exploit the Daleckii-Krein formula in operator theory to give a concise and
unified result on differentiating SPD matrix functions, including the matrix
logarithm to handle the Riemannian geometry of kernel matrix. Experiments not
only show the superiority of kernel-matrix-based SPD representation with deep
local descriptors, but also verify the advantage of the proposed deep network
in pursuing better SPD representations for fine-grained image recognition
tasks
Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models
Nonnegative matrix factorization (NMF) is a powerful class of feature
extraction techniques that has been successfully applied in many fields, namely
in signal and image processing. Current NMF techniques have been limited to a
single-objective problem in either its linear or nonlinear kernel-based
formulation. In this paper, we propose to revisit the NMF as a multi-objective
problem, in particular a bi-objective one, where the objective functions
defined in both input and feature spaces are taken into account. By taking the
advantage of the sum-weighted method from the literature of multi-objective
optimization, the proposed bi-objective NMF determines a set of nondominated,
Pareto optimal, solutions instead of a single optimal decomposition. Moreover,
the corresponding Pareto front is studied and approximated. Experimental
results on unmixing real hyperspectral images confirm the efficiency of the
proposed bi-objective NMF compared with the state-of-the-art methods
Learning Action Maps of Large Environments via First-Person Vision
When people observe and interact with physical spaces, they are able to
associate functionality to regions in the environment. Our goal is to automate
dense functional understanding of large spaces by leveraging sparse activity
demonstrations recorded from an ego-centric viewpoint. The method we describe
enables functionality estimation in large scenes where people have behaved, as
well as novel scenes where no behaviors are observed. Our method learns and
predicts "Action Maps", which encode the ability for a user to perform
activities at various locations. With the usage of an egocentric camera to
observe human activities, our method scales with the size of the scene without
the need for mounting multiple static surveillance cameras and is well-suited
to the task of observing activities up-close. We demonstrate that by capturing
appearance-based attributes of the environment and associating these attributes
with activity demonstrations, our proposed mathematical framework allows for
the prediction of Action Maps in new environments. Additionally, we offer a
preliminary glance of the applicability of Action Maps by demonstrating a
proof-of-concept application in which they are used in concert with activity
detections to perform localization.Comment: To appear at CVPR 201
Beating the Folklore Algorithm for Dynamic Matching
The maximum matching problem in dynamic graphs subject to edge updates (insertions and deletions) has received much attention over the last few years; a multitude of approximation/time tradeoffs were obtained, improving upon the folklore algorithm, which maintains a maximal (and hence 2-approximate) matching in O(n) worst-case update time in n-node graphs.
We present the first deterministic algorithm which outperforms the folklore algorithm in terms of both approximation ratio and worst-case update time. Specifically, we give a (2-?(1))-approximate algorithm with O(m^{3/8}) = O(n^{3/4}) worst-case update time in n-node, m-edge graphs. For sufficiently small constant ? > 0, no deterministic (2+?)-approximate algorithm with worst-case update time O(n^{0.99}) was known. Our second result is the first deterministic (2+?)-approximate weighted matching algorithm with O_?(1)? O(?{m}) = O_?(1)? O(?n) worst-case update time. Neither of our results were previously known to be achievable by a randomized algorithm against an adaptive adversary.
Our main technical contributions are threefold: first, we characterize the tight cases for kernels, which are the well-studied matching sparsifiers underlying much of the (2+?)-approximate dynamic matching literature. This characterization, together with multiple ideas - old and new - underlies our result for breaking the approximation barrier of 2. Our second technical contribution is the first example of a dynamic matching algorithm whose running time is improved due to improving the recourse of other dynamic matching algorithms. Finally, we show how to use dynamic bipartite matching algorithms as black-box subroutines for dynamic matching in general graphs without incurring the natural 3/2 factor in the approximation ratio which such approaches naturally incur (reminiscent of the integrality gap of the fractional matching polytope in general graphs)
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization
Existing Android malware detection approaches use a variety of features such
as security sensitive APIs, system calls, control-flow structures and
information flows in conjunction with Machine Learning classifiers to achieve
accurate detection. Each of these feature sets provides a unique semantic
perspective (or view) of apps' behaviours with inherent strengths and
limitations. Meaning, some views are more amenable to detect certain attacks
but may not be suitable to characterise several other attacks. Most of the
existing malware detection approaches use only one (or a selected few) of the
aforementioned feature sets which prevent them from detecting a vast majority
of attacks. Addressing this limitation, we propose MKLDroid, a unified
framework that systematically integrates multiple views of apps for performing
comprehensive malware detection and malicious code localisation. The rationale
is that, while a malware app can disguise itself in some views, disguising in
every view while maintaining malicious intent will be much harder.
MKLDroid uses a graph kernel to capture structural and contextual information
from apps' dependency graphs and identify malice code patterns in each view.
Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted
combination of the views which yields the best detection accuracy. Besides
multi-view learning, MKLDroid's unique and salient trait is its ability to
locate fine-grained malice code portions in dependency graphs (e.g.,
methods/classes). Through our large-scale experiments on several datasets
(incl. wild apps), we demonstrate that MKLDroid outperforms three
state-of-the-art techniques consistently, in terms of accuracy while
maintaining comparable efficiency. In our malicious code localisation
experiments on a dataset of repackaged malware, MKLDroid was able to identify
all the malice classes with 94% average recall
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