5,346 research outputs found
Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation
Recently, mid-level features have shown promising performance in computer
vision. Mid-level features learned by incorporating class-level information are
potentially more discriminative than traditional low-level local features. In
this paper, an effective method is proposed to extract mid-level features from
Kinect skeletons for 3D human action recognition. Firstly, the orientations of
limbs connected by two skeleton joints are computed and each orientation is
encoded into one of the 27 states indicating the spatial relationship of the
joints. Secondly, limbs are combined into parts and the limb's states are
mapped into part states. Finally, frequent pattern mining is employed to mine
the most frequent and relevant (discriminative, representative and
non-redundant) states of parts in continuous several frames. These parts are
referred to as Frequent Local Parts or FLPs. The FLPs allow us to build
powerful bag-of-FLP-based action representation. This new representation yields
state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D
Continuous Action Recognition Based on Sequence Alignment
Continuous action recognition is more challenging than isolated recognition
because classification and segmentation must be simultaneously carried out. We
build on the well known dynamic time warping (DTW) framework and devise a novel
visual alignment technique, namely dynamic frame warping (DFW), which performs
isolated recognition based on per-frame representation of videos, and on
aligning a test sequence with a model sequence. Moreover, we propose two
extensions which enable to perform recognition concomitant with segmentation,
namely one-pass DFW and two-pass DFW. These two methods have their roots in the
domain of continuous recognition of speech and, to the best of our knowledge,
their extension to continuous visual action recognition has been overlooked. We
test and illustrate the proposed techniques with a recently released dataset
(RAVEL) and with two public-domain datasets widely used in action recognition
(Hollywood-1 and Hollywood-2). We also compare the performances of the proposed
isolated and continuous recognition algorithms with several recently published
methods
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Nowadays, with the availability of massive amount of trade data collected,
the dynamics of the financial markets pose both a challenge and an opportunity
for high frequency traders. In order to take advantage of the rapid, subtle
movement of assets in High Frequency Trading (HFT), an automatic algorithm to
analyze and detect patterns of price change based on transaction records must
be available. The multichannel, time-series representation of financial data
naturally suggests tensor-based learning algorithms. In this work, we
investigate the effectiveness of two multilinear methods for the mid-price
prediction problem against other existing methods. The experiments in a large
scale dataset which contains more than 4 millions limit orders show that by
utilizing tensor representation, multilinear models outperform vector-based
approaches and other competing ones.Comment: accepted in SSCI 2017, typos fixe
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