pwc-archive/papers-with-abstracts huggingface dataset:
An Expressive Deep Model for Human Action Parsing from A Single Image
Authors: Zhujin Liang, Xiaolong Wang, Rui Huang, Liang Lin
Date: 2015-02-02 00:00:00
Tasks: Action Parsing, Action Understanding, object-detection, Object Detection

This paper aims at one newly raising task in vision and multimedia research:
recognizing human actions from still images. Its main challenges lie in the
large variations in human poses and appearances, as well as the lack of
temporal motion information. Addressing these problems, we propose to develop
an expressive deep model to naturally integrate human layout and surrounding
contexts for higher level action understanding from still images. In
particular, a Deep Belief Net is trained to fuse information from different
noisy sources such as body part detection and object detection. To bridge the
semantic gap, we used manually labeled data to greatly improve the
effectiveness and efficiency of the pre-training and fine-tuning stages of the
DBN training. The resulting framework is shown to be robust to sometimes
unreliable inputs (e.g., imprecise detections of human parts and objects), and
outperforms the state-of-the-art approaches.
Fast and Robust Feature Matching for RGB-D Based Localization
Authors: Miguel Heredia, Felix Endres, Wolfram Burgard, Rafael Sanz
Date: 2015-02-02 00:00:00
Tasks: Visual Localization

In this paper we present a novel approach to global localization using an
RGB-D camera in maps of visual features. For large maps, the performance of
pure image matching techniques decays in terms of robustness and computational
cost. Particularly, repeated occurrences of similar features due to repeating
structure in the world (e.g., doorways, chairs, etc.) or missing associations
between observations pose critical challenges to visual localization. We
address these challenges using a two-step approach. We first estimate a
candidate pose using few correspondences between features of the current camera
frame and the feature map. The initial set of correspondences is established by
proximity in feature space. The initial pose estimate is used in the second
step to guide spatial matching of features in 3D, i.e., searching for
associations where the image features are expected to be found in the map. A
RANSAC algorithm is used to compute a fine estimation of the pose from the
correspondences. Our approach clearly outperforms localization based on feature
matching exclusively in feature space, both in terms of estimation accuracy and
robustness to failure and allows for global localization in real time (30Hz).
From neural PCA to deep unsupervised learning
Authors: Harri Valpola
Date: 2014-11-28 00:00:00
Tasks: Decoder, Denoising

A network supporting deep unsupervised learning is presented. The network is
an autoencoder with lateral shortcut connections from the encoder to decoder at
each level of the hierarchy. The lateral shortcut connections allow the higher
levels of the hierarchy to focus on abstract invariant features. While standard
autoencoders are analogous to latent variable models with a single layer of
stochastic variables, the proposed network is analogous to hierarchical latent
variables models. Learning combines denoising autoencoder and denoising sources
separation frameworks. Each layer of the network contributes to the cost
function a term which measures the distance of the representations produced by
the encoder and the decoder. Since training signals originate from all levels
of the network, all layers can learn efficiently even in deep networks. The
speedup offered by cost terms from higher levels of the hierarchy and the
ability to learn invariant features are demonstrated in experiments.
A scaled gradient projection method for Bayesian learning in dynamical systems
Authors: Silvia Bonettini, Alessandro Chiuso, Marco Prato
Date: 2014-06-25 00:00:00
Tasks: Second-order methods

A crucial task in system identification problems is the selection of the most
appropriate model class, and is classically addressed resorting to
cross-validation or using asymptotic arguments. As recently suggested in the
literature, this can be addressed in a Bayesian framework, where model
complexity is regulated by few hyperparameters, which can be estimated via
marginal likelihood maximization. It is thus of primary importance to design
effective optimization methods to solve the corresponding optimization problem.
If the unknown impulse response is modeled as a Gaussian process with a
suitable kernel, the maximization of the marginal likelihood leads to a
challenging nonconvex optimization problem, which requires a stable and
effective solution strategy. In this paper we address this problem by means of
a scaled gradient projection algorithm, in which the scaling matrix and the
steplength parameter play a crucial role to provide a meaning solution in a
computational time comparable with second order methods. In particular, we
propose both a generalization of the split gradient approach to design the
scaling matrix in the presence of box constraints, and an effective
implementation of the gradient and objective function. The extensive numerical
experiments carried out on several test problems show that our method is very
effective in providing in few tenths of a second solutions of the problems with
accuracy comparable with state-of-the-art approaches. Moreover, the flexibility
of the proposed strategy makes it easily adaptable to a wider range of problems
arising in different areas of machine learning, signal processing and system
identification.
Towards a solid solution of real-time fire and flame detection
Authors: Bo Jiang, Yongyi Lu, Xiying Li, Liang Lin
Date: 2015-02-02 00:00:00
Tasks: object-detection, Object Detection, Retrieval

Although the object detection and recognition has received growing attention
for decades, a robust fire and flame detection method is rarely explored. This
paper presents an empirical study, towards a general and solid approach to fast
detect fire and flame in videos, with the applications in video surveillance
and event retrieval. Our system consists of three cascaded steps: (1) candidate
regions proposing by a background model, (2) fire region classifying with
color-texture features and a dictionary of visual words, and (3) temporal
verifying. The experimental evaluation and analysis are done for each step. We
believe that it is a useful service to both academic research and real-world
application. In addition, we release the software of the proposed system with
the source code, as well as a public benchmark and data set, including 64 video
clips covered both indoor and outdoor scenes under different conditions. We
achieve an 82% Recall with 93% Precision on the data set, and greatly improve
the performance by state-of-the-arts methods.
Info-Greedy sequential adaptive compressed sensing
Authors: Gabor Braun, Sebastian Pokutta, Yao Xie
Date: 2014-07-02 00:00:00
Tasks: compressed sensing

We present an information-theoretic framework for sequential adaptive
compressed sensing, Info-Greedy Sensing, where measurements are chosen to
maximize the extracted information conditioned on the previous measurements. We
show that the widely used bisection approach is Info-Greedy for a family of
$k$-sparse signals by connecting compressed sensing and blackbox complexity of
sequential query algorithms, and present Info-Greedy algorithms for Gaussian
and Gaussian Mixture Model (GMM) signals, as well as ways to design sparse
Info-Greedy measurements. Numerical examples demonstrate the good performance
of the proposed algorithms using simulated and real data: Info-Greedy Sensing
shows significant improvement over random projection for signals with sparse
and low-rank covariance matrices, and adaptivity brings robustness when there
is a mismatch between the assumed and the true distributions.
Integrating Graph Partitioning and Matching for Trajectory Analysis in Video Surveillance
Authors: Liang Lin, Yongyi Lu, Yan Pan, Xiaowu Chen
Date: 2015-02-02 00:00:00
Tasks: Attribute, Graph Matching, graph partitioning

In order to track the moving objects in long range against occlusion,
interruption, and background clutter, this paper proposes a unified approach
for global trajectory analysis. Instead of the traditional frame-by-frame
tracking, our method recovers target trajectories based on a short sequence of
video frames, e.g. $15$ frames. We initially calculate a foreground map at each
frame, as obtained from a state-of-the-art background model. An attribute graph
is then extracted from the foreground map, where the graph vertices are image
primitives represented by the composite features. With this graph
representation, we pose trajectory analysis as a joint task of spatial graph
partitioning and temporal graph matching. The task can be formulated by
maximizing a posteriori under the Bayesian framework, in which we integrate the
spatio-temporal contexts and the appearance models. The probabilistic inference
is achieved by a data-driven Markov Chain Monte Carlo (MCMC) algorithm. Given a
peroid of observed frames, the algorithm simulates a ergodic and aperiodic
Markov Chain, and it visits a sequence of solution states in the joint space of
spatial graph partitioning and temporal graph matching. In the experiments, our
method is tested on several challenging videos from the public datasets of
visual surveillance, and it outperforms the state-of-the-art methods.
Adaptive Scene Category Discovery with Generative Learning and Compositional Sampling
Authors: Liang Lin, Ruimao Zhang, Xiaohua Duan
Date: 2015-02-02 00:00:00
Tasks: Image Categorization

This paper investigates a general framework to discover categories of
unlabeled scene images according to their appearances (i.e., textures and
structures). We jointly solve the two coupled tasks in an unsupervised manner:
(i) classifying images without pre-determining the number of categories, and
(ii) pursuing generative model for each category. In our method, each image is
represented by two types of image descriptors that are effective to capture
image appearances from different aspects. By treating each image as a graph
vertex, we build up an graph, and pose the image categorization as a graph
partition process. Specifically, a partitioned sub-graph can be regarded as a
category of scenes, and we define the probabilistic model of graph partition by
accumulating the generative models of all separated categories. For efficient
inference with the graph, we employ a stochastic cluster sampling algorithm,
which is designed based on the Metropolis-Hasting mechanism. During the
iterations of inference, the model of each category is analytically updated by
a generative learning algorithm. In the experiments, our approach is validated
on several challenging databases, and it outperforms other popular
state-of-the-art methods. The implementation details and empirical analysis are
presented as well.
Iterated Support Vector Machines for Distance Metric Learning
Authors: Wangmeng Zuo, Faqiang Wang, David Zhang, Liang Lin, Yuchi Huang, Deyu Meng, Lei Zhang
Date: 2015-02-02 00:00:00
Tasks: Classification, Face Verification, General Classification, Handwritten Digit Recognition, Metric Learning, Person Re-Identification, valid

Distance metric learning aims to learn from the given training data a valid
distance metric, with which the similarity between data samples can be more
effectively evaluated for classification. Metric learning is often formulated
as a convex or nonconvex optimization problem, while many existing metric
learning algorithms become inefficient for large scale problems. In this paper,
we formulate metric learning as a kernel classification problem, and solve it
by iterated training of support vector machines (SVM). The new formulation is
easy to implement, efficient in training, and tractable for large-scale
problems. Two novel metric learning models, namely Positive-semidefinite
Constrained Metric Learning (PCML) and Nonnegative-coefficient Constrained
Metric Learning (NCML), are developed. Both PCML and NCML can guarantee the
global optimality of their solutions. Experimental results on UCI dataset
classification, handwritten digit recognition, face verification and person
re-identification demonstrate that the proposed metric learning methods achieve
higher classification accuracy than state-of-the-art methods and they are
significantly more efficient in training.
A Web-based Interactive Visual Graph Analytics Platform
Authors: Nesreen K. Ahmed, Ryan A. Rossi
Date: 2015-02-02 00:00:00
Tasks: Community Detection, Decision Making, Graph Mining

This paper proposes a web-based visual graph analytics platform for
interactive graph mining, visualization, and real-time exploration of networks.
GraphVis is fast, intuitive, and flexible, combining interactive visualizations
with analytic techniques to reveal important patterns and insights for sense
making, reasoning, and decision making. Networks can be visualized and explored
within seconds by simply drag-and-dropping a graph file into the web browser.
The structure, properties, and patterns of the network are computed
automatically and can be instantly explored in real-time. At the heart of
GraphVis lies a multi-level interactive network visualization and analytics
engine that allows for real-time graph mining and exploration across multiple
levels of granularity simultaneously. Both the graph analytic and visualization
techniques (at each level of granularity) are dynamic and interactive, with
immediate and continuous visual feedback upon every user interaction (e.g.,
change of a slider for filtering). Furthermore, nodes, edges, and subgraphs are
easily inserted, deleted or exported via a number of novel techniques and tools
that make it extremely easy and flexible for exploring, testing hypothesis, and
understanding networks in real-time over the web. A number of interactive
visual graph analytic techniques are also proposed including interactive role
discovery methods, community detection, as well as a number of novel block
models for generating graphs with community structure. Finally, we also
highlight other key aspects including filtering, querying, ranking,
manipulating, exporting, partitioning, as well as tools for dynamic network
analysis and visualization, interactive graph generators, and a variety of
multi-level network analysis, summarization, and statistical techniques.
Complex Background Subtraction by Pursuing Dynamic Spatio-Temporal Models
Authors: Liang Lin, Yuanlu Xu, Xiaodan Liang, Jian-Huang Lai
Date: 2015-02-02 00:00:00

Although it has been widely discussed in video surveillance, background
subtraction is still an open problem in the context of complex scenarios, e.g.,
dynamic backgrounds, illumination variations, and indistinct foreground
objects. To address these challenges, we propose an effective background
subtraction method by learning and maintaining an array of dynamic texture
models within the spatio-temporal representations. At any location of the
scene, we extract a sequence of regular video bricks, i.e. video volumes
spanning over both spatial and temporal domain. The background modeling is thus
posed as pursuing subspaces within the video bricks while adapting the scene
variations. For each sequence of video bricks, we pursue the subspace by
employing the ARMA (Auto Regressive Moving Average) Model that jointly
characterizes the appearance consistency and temporal coherence of the
observations. During online processing, we incrementally update the subspaces
to cope with disturbances from foreground objects and scene changes. In the
experiments, we validate the proposed method in several complex scenarios, and
show superior performances over other state-of-the-art approaches of background
subtraction. The empirical studies of parameter setting and component analysis
are presented as well.
Discriminatively Trained And-Or Graph Models for Object Shape Detection
Authors: Liang Lin, Xiaolong Wang, Wei Yang, Jian-Huang Lai
Date: 2015-02-02 00:00:00
Tasks: object-detection, Object Detection

In this paper, we investigate a novel reconfigurable part-based model, namely
And-Or graph model, to recognize object shapes in images. Our proposed model
consists of four layers: leaf-nodes at the bottom are local classifiers for
detecting contour fragments; or-nodes above the leaf-nodes function as the
switches to activate their child leaf-nodes, making the model reconfigurable
during inference; and-nodes in a higher layer capture holistic shape
deformations; one root-node on the top, which is also an or-node, activates one
of its child and-nodes to deal with large global variations (e.g. different
poses and views). We propose a novel structural optimization algorithm to
discriminatively train the And-Or model from weakly annotated data. This
algorithm iteratively determines the model structures (e.g. the nodes and their
layouts) along with the parameter learning. On several challenging datasets,
our model demonstrates the effectiveness to perform robust shape-based object
detection against background clutter and outperforms the other state-of-the-art
approaches. We also release a new shape database with annotations, which
includes more than 1500 challenging shape instances, for recognition and
detection.
Modified Fast Fractal Image Compression Algorithm in spatial domain
Authors: M. Salarian, H. Miar Naimi
Date: 2015-02-01 00:00:00
Tasks: Image Compression

In this paper a new fractal image compression algorithm is proposed in which
the time of encoding process is considerably reduced. The algorithm exploits a
domain pool reduction approach, along with using innovative predefined values
for contrast scaling factor, S, instead of searching it across [0,1]. Only the
domain blocks with entropy greater than a threshold are considered as domain
pool. As a novel point, it is assumed that in each step of the encoding
process, the domain block with small enough distance shall be found only for
the range blocks with low activity (equivalently low entropy). This novel point
is used to find reasonable estimations of S, and use them in the encoding
process as predefined values, mentioned above, the remaining range blocks are
split into four new smaller range blocks and the algorithm must be iterated for
them, considered as the other step of encoding process. The algorithm has been
examined for some of the well-known images and the results have been compared
with the state-of-the-art algorithms. The experiments show that our proposed
algorithm has considerably lower encoding time than the other where the encoded
images are approximately the same in quality.
Dynamic texture and scene classification by transferring deep image features
Authors: Xianbiao Qi, Chun-Guang Li, Guoying Zhao, Xiaopeng Hong, Matti Pietikäinen
Date: 2015-02-01 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Scene Classification

Dynamic texture and scene classification are two fundamental problems in
understanding natural video content. Extracting robust and effective features
is a crucial step towards solving these problems. However the existing
approaches suffer from the sensitivity to either varying illumination, or
viewpoint changing, or even camera motion, and/or the lack of spatial
information. Inspired by the success of deep structures in image
classification, we attempt to leverage a deep structure to extract feature for
dynamic texture and scene classification. To tackle with the challenges in
training a deep structure, we propose to transfer some prior knowledge from
image domain to video domain. To be specific, we propose to apply a
well-trained Convolutional Neural Network (ConvNet) as a mid-level feature
extractor to extract features from each frame, and then form a representation
of a video by concatenating the first and the second order statistics over the
mid-level features. We term this two-level feature extraction scheme as a
Transferred ConvNet Feature (TCoF). Moreover we explore two different
implementations of the TCoF scheme, i.e., the \textit{spatial} TCoF and the
\textit{temporal} TCoF, in which the mean-removed frames and the difference
between two adjacent frames are used as the inputs of the ConvNet,
respectively. We evaluate systematically the proposed spatial TCoF and the
temporal TCoF schemes on three benchmark data sets, including DynTex, YUPENN,
and Maryland, and demonstrate that the proposed approach yields superior
performance.
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks
Authors: Keze Wang, Xiaolong Wang, Liang Lin, Meng Wang, WangMeng Zuo
Date: 2015-01-26 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Human activity understanding with 3D/depth sensors has received increasing
attention in multimedia processing and interactions. This work targets on
developing a novel deep model for automatic activity recognition from RGB-D
videos. We represent each human activity as an ensemble of cubic-like video
segments, and learn to discover the temporal structures for a category of
activities, i.e. how the activities to be decomposed in terms of
classification. Our model can be regarded as a structured deep architecture, as
it extends the convolutional neural networks (CNNs) by incorporating structure
alternatives. Specifically, we build the network consisting of 3D convolutions
and max-pooling operators over the video segments, and introduce the latent
variables in each convolutional layer manipulating the activation of neurons.
Our model thus advances existing approaches in two aspects: (i) it acts
directly on the raw inputs (grayscale-depth data) to conduct recognition
instead of relying on hand-crafted features, and (ii) the model structure can
be dynamically adjusted accounting for the temporal variations of human
activities, i.e. the network configuration is allowed to be partially activated
during inference. For model training, we propose an EM-type optimization method
that iteratively (i) discovers the latent structure by determining the
decomposed actions for each training example, and (ii) learns the network
parameters by using the back-propagation algorithm. Our approach is validated
in challenging scenarios, and outperforms state-of-the-art methods. A large
human activity database of RGB-D videos is presented in addition.
Learning Latent Spatio-Temporal Compositional Model for Human Action Recognition
Authors: Xiaodan Liang, Liang Lin, Liangliang Cao
Date: 2015-02-01 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Weakly-supervised Learning

Action recognition is an important problem in multimedia understanding. This
paper addresses this problem by building an expressive compositional action
model. We model one action instance in the video with an ensemble of
spatio-temporal compositions: a number of discrete temporal anchor frames, each
of which is further decomposed to a layout of deformable parts. In this way,
our model can identify a Spatio-Temporal And-Or Graph (STAOG) to represent the
latent structure of actions e.g. triple jumping, swinging and high jumping. The
STAOG model comprises four layers: (i) a batch of leaf-nodes in bottom for
detecting various action parts within video patches; (ii) the or-nodes over
bottom, i.e. switch variables to activate their children leaf-nodes for
structural variability; (iii) the and-nodes within an anchor frame for
verifying spatial composition; and (iv) the root-node at top for aggregating
scores over temporal anchor frames. Moreover, the contextual interactions are
defined between leaf-nodes in both spatial and temporal domains. For model
training, we develop a novel weakly supervised learning algorithm which
iteratively determines the structural configuration (e.g. the production of
leaf-nodes associated with the or-nodes) along with the optimization of
multi-layer parameters. By fully exploiting spatio-temporal compositions and
interactions, our approach handles well large intra-class action variance (e.g.
different views, individual appearances, spatio-temporal structures). The
experimental results on the challenging databases demonstrate superior
performance of our approach over other competing methods.