53 research outputs found
Investigation of new feature descriptors for image search and classification
Content-based image search, classification and retrieval is an active and important research area due to its broad applications as well as the complexity of the problem. Understanding the semantics and contents of images for recognition remains one of the most difficult and prevailing problems in the machine intelligence and computer vision community. With large variations in size, pose, illumination and occlusions, image classification is a very challenging task. A good classification framework should address the key issues of discriminatory feature extraction as well as efficient and accurate classification. Towards that end, this dissertation focuses on exploring new image descriptors by incorporating cues from the human visual system, and integrating local, texture, shape as well as color information to construct robust and effective feature representations for advancing content-based image search and classification.
Based on the Gabor wavelet transformation, whose kernels are similar to the 2D receptive field profiles of the mammalian cortical simple cells, a series of new image descriptors is developed. Specifically, first, a new color Gabor-HOG (GHOG) descriptor is introduced by concatenating the Histograms of Oriented Gradients (HOG) of the component images produced by applying Gabor filters in multiple scales and orientations to encode shape information. Second, the GHOG descriptor is analyzed in six different color spaces and grayscale to propose different color GHOG descriptors, which are further combined to present a new Fused Color GHOG (FC-GHOG) descriptor. Third, a novel GaborPHOG (GPHOG) descriptor is proposed which improves upon the Pyramid Histograms of Oriented Gradients (PHOG) descriptor, and subsequently a new FC-GPHOG descriptor is constructed by combining the multiple color GPHOG descriptors and employing the Principal Component Analysis (PCA). Next, the Gabor-LBP (GLBP) is derived by accumulating the Local Binary Patterns (LBP) histograms of the local Gabor filtered images to encode texture and local information of an image. Furthermore, a novel Gabor-LBPPHOG (GLP) image descriptor is proposed which integrates the GLBP and the GPHOG descriptors as a feature set and an innovative Fused Color Gabor-LBP-PHOG (FC-GLP) is constructed by fusing the GLP from multiple color spaces. Subsequently, The GLBP and the GHOG descriptors are then combined to produce the Gabor-LBP-HOG (GLH) feature vector which performs well on different object and scene image categories. The six color GLH vectors are further concatenated to form the Fused Color GLH (FC-GLH) descriptor. Finally, the Wigner based Local Binary Patterns (WLBP) descriptor is proposed that combines multi-neighborhood LBP, Pseudo-Wigner distribution of images and the popular bag of words model to effectively classify scene images.
To assess the feasibility of the proposed new image descriptors, two classification methods are used: one method applies the PCA and the Enhanced Fisher Model (EFM) for feature extraction and the nearest neighbor rule for classification, while the other method employs the Support Vector Machine (SVM). The classification performance of the proposed descriptors is tested on several publicly available popular image datasets. The experimental results show that the proposed new image descriptors achieve image search and classification results better than or at par with other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT), the Pyramid Histograms of visual Words (PHOW), the Pyramid Histograms of Oriented Gradients (PHOG), the Spatial Envelope (SE), the Color SIFT four Concentric Circles (C4CC), the Object Bank (OB), the Context Aware Topic Model (CA-TM), the Hierarchical Matching Pursuit (HMP), the Kernel Spatial Pyramid Matching (KSPM), the SIFT Sparse-coded Spatial Pyramid Matching (Sc-SPM), the Kernel Codebook (KC) and the LBP
Study of underlying particle spectrum during huge X-ray flare of Mkn 421 in April 2013
Context: In April 2013, the nearby (z=0.031) TeV blazar, Mkn 421, showed one
of the largest flares in X-rays since the past decade. Aim: To study all
multiwavelength data available during MJD 56392 to 56403, with special emphasis
on X-ray data, and understand the underlying particle energy distribution.
Methods: We study the correlations between the UV and gamma bands with the
X-ray band using the z-transformed discrete correlation function. We model the
underlying particle spectrum with a single population of electrons emitting
synchrotron radiation, and do a statistical fitting of the simultaneous,
time-resolved data from the Swift-XRT and the NuSTAR. Results: There was rapid
flux variability in the X-ray band, with a minimum doubling timescale of hrs. There were no corresponding flares in UV and gamma bands. The
variability in UV and gamma rays are relatively modest with and
respectively, and no significant correlation was found with the
X-ray light curve. The observed X-ray spectrum shows clear curvature which can
be fit by a log parabolic spectral form. This is best explained to originate
from a log parabolic electron spectrum. However, a broken power law or a power
law with an exponentially falling electron distribution cannot be ruled out
either. Moreover, the excellent broadband spectrum from keV allows us
to make predictions of the UV flux. We find that this prediction is compatible
with the observed flux during the low state in X-rays. However, during the
X-ray flares, the predicted flux is a factor of smaller than the
observed one. This suggests that the X-ray flares are plausibly caused by a
separate population which does not contribute significantly to the radiation at
lower energies. Alternatively, the underlying particle spectrum can be much
more complex than the ones explored in this work.Comment: 11 pages, 7 figures, Accepted in A&
CTA IRF current data format and model - missing features and open issues
This document reports open points, issues and proposed new features of the CTA IRF data format collected by the ASWG IRF task group while mapping the Prod5 IRF data format against the GADF1 one, and its review by the ASWG members during the ASWG call on the 28th of April 2022
Performance update of an event-type based analysis for the Cherenkov Telescope Array
The Cherenkov Telescope Array (CTA) will be the next-generation observatory
in the field of very-high-energy (20 GeV to 300 TeV) gamma-ray astroparticle
physics. The traditional approach to data analysis in this field is to apply
quality cuts, optimized using Monte Carlo simulations, on the data acquired to
maximize sensitivity. Subsequent steps of the analysis typically use the
surviving events to calculate one set of instrument response functions (IRFs)
to physically interpret the results. However, an alternative approach is the
use of event types, as implemented in experiments such as the Fermi-LAT. This
approach divides events into sub-samples based on their reconstruction quality,
and a set of IRFs is calculated for each sub-sample. The sub-samples are then
combined in a joint analysis, treating them as independent observations. In
previous works we demonstrated that event types, classified using Machine
Learning methods according to their expected angular reconstruction quality,
have the potential to significantly improve the CTA angular and energy
resolution of a point-like source analysis. Now, we validated the production of
event-type wise full-enclosure IRFs, ready to be used with science tools (such
as Gammapy and ctools). We will report on the impact of using such an
event-type classification on CTA high-level performance, compared to the
traditional procedure.Comment: 7 pages, 3 figures, Presented at the 38th International Cosmic Ray
Conference (ICRC 2023), 2023 (arXiv:submit/2309.08219
Gammapy: A Python package for gamma-ray astronomy
In this article, we present Gammapy, an open-source Python package for the
analysis of astronomical -ray data, and illustrate the functionalities
of its first long-term-support release, version 1.0. Built on the modern Python
scientific ecosystem, Gammapy provides a uniform platform for reducing and
modeling data from different -ray instruments for many analysis
scenarios. Gammapy complies with several well-established data conventions in
high-energy astrophysics, providing serialized data products that are
interoperable with other software packages. Starting from event lists and
instrument response functions, Gammapy provides functionalities to reduce these
data by binning them in energy and sky coordinates. Several techniques for
background estimation are implemented in the package to handle the residual
hadronic background affecting -ray instruments. After the data are
binned, the flux and morphology of one or more -ray sources can be
estimated using Poisson maximum likelihood fitting and assuming a variety of
spectral, temporal, and spatial models. Estimation of flux points, likelihood
profiles, and light curves is also supported. After describing the structure of
the package, we show, using publicly available -ray data, the
capabilities of Gammapy in multiple traditional and novel -ray analysis
scenarios, such as spectral and spectro-morphological modeling and estimations
of a spectral energy distribution and a light curve. Its flexibility and power
are displayed in a final multi-instrument example, where datasets from
different instruments, at different stages of data reduction, are
simultaneously fitted with an astrophysical flux model.Comment: 26 pages, 16 figure
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