115,237 research outputs found
Sparse and low rank approximations for action recognition
Action recognition is crucial area of research in computer vision with wide range of
applications in surveillance, patient-monitoring systems, video indexing, Human-
Computer Interaction and many more. These applications require automated
action recognition. Robust classification methods are sought-after despite influential
research in this field over past decade. The data resources have grown
tremendously owing to the advances in the digital revolution which cannot be
compared to the meagre resources in the past. The main limitation on a system
when dealing with video data is the computational burden due to large dimensions
and data redundancy. Sparse and low rank approximation methods have evolved
recently which aim at concise and meaningful representation of data. This thesis
explores the application of sparse and low rank approximation methods in the
context of video data classification with the following contributions.
1. An approach for solving the problem of action and gesture classification is
proposed within the sparse representation domain, effectively dealing with
large feature dimensions,
2. Low rank matrix completion approach is proposed to jointly classify more
than one action
3. Deep features are proposed for robust classification of multiple actions
within matrix completion framework which can handle data deficiencies.
This thesis starts with the applicability of sparse representations based classifi-
cation methods to the problem of action and gesture recognition. Random projection
is used to reduce the dimensionality of the features. These are referred
to as compressed features in this thesis. The dictionary formed with compressed
features has proved to be efficient for the classification task achieving comparable
results to the state of the art.
Next, this thesis addresses the more promising problem of simultaneous classifi-
cation of multiple actions. This is treated as matrix completion problem under
transduction setting. Matrix completion methods are considered as the generic
extension to the sparse representation methods from compressed sensing point
of view. The features and corresponding labels of the training and test data are
concatenated and placed as columns of a matrix. The unknown test labels would
be the missing entries in that matrix. This is solved using rank minimization
techniques based on the assumption that the underlying complete matrix would
be a low rank one. This approach has achieved results better than the state of the art on datasets with varying complexities.
This thesis then extends the matrix completion framework for joint classification
of actions to handle the missing features besides missing test labels. In
this context, deep features from a convolutional neural network are proposed.
A convolutional neural network is trained on the training data and features are
extracted from train and test data from the trained network. The performance
of the deep features has proved to be promising when compared to the state of
the art hand-crafted features
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Coronavirus disease (Covid-19) has been the main agenda of the whole world
since it came in sight in December 2019. It has already caused thousands of
causalities and infected several millions worldwide. Any technological tool
that can be provided to healthcare practitioners to save time, effort, and
possibly lives has crucial importance. The main tools practitioners currently
use to diagnose Covid-19 are Reverse Transcription-Polymerase Chain reaction
(RT-PCR) and Computed Tomography (CT), which require significant time,
resources and acknowledged experts. X-ray imaging is a common and easily
accessible tool that has great potential for Covid-19 diagnosis. In this study,
we propose a novel approach for Covid-19 recognition from chest X-ray images.
Despite the importance of the problem, recent studies in this domain produced
not so satisfactory results due to the limited datasets available for training.
Recall that Deep Learning techniques can generally provide state-of-the-art
performance in many classification tasks when trained properly over large
datasets, such data scarcity can be a crucial obstacle when using them for
Covid-19 detection. Alternative approaches such as representation-based
classification (collaborative or sparse representation) might provide
satisfactory performance with limited size datasets, but they generally fall
short in performance or speed compared to Machine Learning methods. To address
this deficiency, Convolution Support Estimation Network (CSEN) has recently
been proposed as a bridge between model-based and Deep Learning approaches by
providing a non-iterative real-time mapping from query sample to ideally sparse
representation coefficient' support, which is critical information for class
decision in representation based techniques.Comment: 10 page
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