32 research outputs found
Compressively Sensed Image Recognition
Compressive Sensing (CS) theory asserts that sparse signal reconstruction is
possible from a small number of linear measurements. Although CS enables
low-cost linear sampling, it requires non-linear and costly reconstruction.
Recent literature works show that compressive image classification is possible
in CS domain without reconstruction of the signal. In this work, we introduce a
DCT base method that extracts binary discriminative features directly from CS
measurements. These CS measurements can be obtained by using (i) a random or a
pseudo-random measurement matrix, or (ii) a measurement matrix whose elements
are learned from the training data to optimize the given classification task.
We further introduce feature fusion by concatenating Bag of Words (BoW)
representation of our binary features with one of the two state-of-the-art
CNN-based feature vectors. We show that our fused feature outperforms the
state-of-the-art in both cases.Comment: 6 pages, submitted/accepted, EUVIP 201
Operational Support Estimator Networks
In this work, we propose a novel approach called Operational Support
Estimator Networks (OSENs) for the support estimation task. Support Estimation
(SE) is defined as finding the locations of non-zero elements in a sparse
signal. By its very nature, the mapping between the measurement and sparse
signal is a non-linear operation. Traditional support estimators rely on
computationally expensive iterative signal recovery techniques to achieve such
non-linearity. Contrary to the convolution layers, the proposed OSEN approach
consists of operational layers that can learn such complex non-linearities
without the need for deep networks. In this way, the performance of the
non-iterative support estimation is greatly improved. Moreover, the operational
layers comprise so-called generative \textit{super neurons} with non-local
kernels. The kernel location for each neuron/feature map is optimized jointly
for the SE task during the training. We evaluate the OSENs in three different
applications: i. support estimation from Compressive Sensing (CS) measurements,
ii. representation-based classification, and iii. learning-aided CS
reconstruction where the output of OSENs is used as prior knowledge to the CS
algorithm for an enhanced reconstruction. Experimental results show that the
proposed approach achieves computational efficiency and outperforms competing
methods, especially at low measurement rates by a significant margin. The
software implementation is publicly shared at
https://github.com/meteahishali/OSEN
Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing
Support estimation (SE) of a sparse signal refers to finding the location
indices of the non-zero elements in a sparse representation. Most of the
traditional approaches dealing with SE problem are iterative algorithms based
on greedy methods or optimization techniques. Indeed, a vast majority of them
use sparse signal recovery techniques to obtain support sets instead of
directly mapping the non-zero locations from denser measurements (e.g.,
Compressively Sensed Measurements). This study proposes a novel approach for
learning such a mapping from a training set. To accomplish this objective, the
Convolutional Support Estimator Networks (CSENs), each with a compact
configuration, are designed. The proposed CSEN can be a crucial tool for the
following scenarios: (i) Real-time and low-cost support estimation can be
applied in any mobile and low-power edge device for anomaly localization,
simultaneous face recognition, etc. (ii) CSEN's output can directly be used as
"prior information" which improves the performance of sparse signal recovery
algorithms. The results over the benchmark datasets show that state-of-the-art
performance levels can be achieved by the proposed approach with a
significantly reduced computational complexity
Parameter estimation validity and relationship robustness: A comparison of telephone and internet survey techniques
With the expansion of telecommunication and online technologies for the purpose of survey administration, the issue of measurement validity has come to the fore. The proliferation of automated audio services and computer-based survey techniques has been matched by a corresponding denigration of the quality of traditional phone survey data, most notably as an outcome of falling response rates. This trend, combined with the introduction of screening technologies and answering machines, represents a barrier to the proper execution of survey research. Whereas the question was once, “can technology-assisted surveys achieve the same level of validity as traditional phone surveys?”, the question now becomes, “what are the relative advantages and disadvantages of technology-assisted and phone surveys?” Each has its own challenges and opportunities, and this paper begins to explore these. The present study provides further insight into the validity of telephone and Internet survey data, and explores whether or not the robustness of relationships between variables varies by survey mode. Study data were provided by two surveys, the first of which was conducted in a metropolitan area of the Midwestern US, with interviews of 505 adults using a computer-aided telephone-interviewing (CATI) system. The second was a national survey of 2172 respondents conducted over the Internet by a commercial research firm that sends requests to a diverse set of potential respondents, who logged onto the survey site to participate. Results suggest that weighting in an attempt to achieve parametric matching does seem to increase robustness of relationships and, in this age of poor response rates, this seems to demand an increased use of parametric weightings. Implications of study findings for telematic survey practitioners are discussed
Representation based regression for object distance estimation
In this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional Support Estimator Networks (CSENs). CSENs are designed to compute a direct mapping for the Support Estimation (SE) task in a representation-based classification problem. We further propose and demonstrate that representation-based methods (sparse or collaborative representation) can be used in well-designed regression problems especially over scarce data. To the best of our knowledge, this is the first representation-based method proposed for performing a regression task by utilizing the modified CSENs; and hence, we name this novel approach as Representation-based Regression (RbR). The initial version of CSENs has a proxy mapping stage (i.e., a coarse estimation for the support set) that is required for the input. In this study, we improve the CSEN model by proposing Compressive Learning CSEN (CL-CSEN) that has the ability to jointly optimize the so-called proxy mapping stage along with convolutional layers. The experimental evaluations using the KITTI 3D Object Detection distance estimation dataset show that the proposed method can achieve a significantly improved distance estimation performance over all competing methods. Finally, the software implementations of the methods are publicly shared at https://github.com/meteahishali/CSENDistance.publishedVersionPeer reviewe
Operational Neural Networks for Efficient Hyperspectral Single-Image Super-Resolution
Hyperspectral Imaging is a crucial tool in remote sensing which captures far
more spectral information than standard color images. However, the increase in
spectral information comes at the cost of spatial resolution. Super-resolution
is a popular technique where the goal is to generate a high-resolution version
of a given low-resolution input. The majority of modern super-resolution
approaches use convolutional neural networks. However, convolution itself is a
linear operation and the networks rely on the non-linear activation functions
after each layer to provide the necessary non-linearity to learn the complex
underlying function. This means that convolutional neural networks tend to be
very deep to achieve the desired results. Recently, self-organized operational
neural networks have been proposed that aim to overcome this limitation by
replacing the convolutional filters with learnable non-linear functions through
the use of MacLaurin series expansions. This work focuses on extending the
convolutional filters of a popular super-resolution model to more powerful
operational filters to enhance the model performance on hyperspectral images.
We also investigate the effects that residual connections and different
normalization types have on this type of enhanced network. Despite having fewer
parameters than their convolutional network equivalents, our results show that
operational neural networks achieve superior super-resolution performance on
small hyperspectral image datasets.Comment: 12 pages, 7 figure
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