2,688 research outputs found
Modelling local phase of images and textures with applications in phase denoising and phase retrieval
The Fourier magnitude has been studied extensively, but less effort has been
devoted to the Fourier phase, despite its well-established importance in image
representation. Global phase was shown to be more important for image
representation than the magnitude, whereas local phase, exhibited in Gabor
filters, has been used for analysis purposes in detecting image contours and
edges. Neither global nor local phase has been modelled in closed form,
suitable for Bayesian estimation.
In this work, we analyze the local phase of textured images and propose a
local (Markovian) model for local phase coefficients. This model is
Gaussian-mixture-based, learned from the graph representation of images, based
on their complex wavelet decomposition. We demonstrate the applicability of the
model in restoration of images with noisy local phase and in image retrieval,
where we show superior performance to the well-known hybrid input-output (HIO)
method. We also provide a framework for application of the model in a general
setup of image processing
Texture retrieval using periodically extended and adaptive curvelets
Image retrieval is an important problem in the area of multimedia processing.
This paper presents two new curvelet-based algorithms for texture retrieval
which are suitable for use in constrained-memory devices. The developed
algorithms are tested on three publicly available texture datasets: CUReT,
Mondial-Marmi, and STex-fabric. Our experiments confirm the effectiveness of
the proposed system. Furthermore, a weighted version of the proposed retrieval
algorithm is proposed, which is shown to achieve promising results in the
classification of seismic activities
Texture Retrieval via the Scattering Transform
This work studies the problem of content-based image retrieval, specifically,
texture retrieval. It focuses on feature extraction and similarity measure for
texture images. Our approach employs a recently developed method, the so-called
Scattering transform, for the process of feature extraction in texture
retrieval. It shares a distinctive property of providing a robust
representation, which is stable with respect to spatial deformations. Recent
work has demonstrated its capability for texture classification, and hence as a
promising candidate for the problem of texture retrieval.
Moreover, we adopt a common approach of measuring the similarity of textures
by comparing the subband histograms of a filterbank transform. To this end we
derive a similarity measure based on the popular Bhattacharyya Kernel. Despite
the popularity of describing histograms using parametrized probability density
functions, such as the Generalized Gaussian Distribution, it is unfortunately
not applicable for describing most of the Scattering transform subbands, due to
the complex modulus performed on each one of them. In this work, we propose to
use the Weibull distribution to model the Scattering subbands of descendant
layers.
Our numerical experiments demonstrated the effectiveness of the proposed
approach, in comparison with several state of the arts
Content-adaptive non-parametric texture similarity measure
In this paper, we introduce a non-parametric texture similarity measure based
on the singular value decomposition of the curvelet coefficients followed by a
content-based truncation of the singular values. This measure focuses on images
with repeating structures and directional content such as those found in
natural texture images. Such textural content is critical for image perception
and its similarity plays a vital role in various computer vision applications.
In this paper, we evaluate the effectiveness of the proposed measure using a
retrieval experiment. The proposed measure outperforms the state-of-the-art
texture similarity metrics on CURet and PerTEx texture databases, respectively.Comment: 7 pages, 7 Figures, 2016 IEEE 18th International Workshop on
Multimedia Signal Processing (MMSP
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Texture is a fundamental characteristic of many types of images, and texture
representation is one of the essential and challenging problems in computer
vision and pattern recognition which has attracted extensive research
attention. Since 2000, texture representations based on Bag of Words (BoW) and
on Convolutional Neural Networks (CNNs) have been extensively studied with
impressive performance. Given this period of remarkable evolution, this paper
aims to present a comprehensive survey of advances in texture representation
over the last two decades. More than 200 major publications are cited in this
survey covering different aspects of the research, which includes (i) problem
description; (ii) recent advances in the broad categories of BoW-based,
CNN-based and attribute-based methods; and (iii) evaluation issues,
specifically benchmark datasets and state of the art results. In retrospect of
what has been achieved so far, the survey discusses open challenges and
directions for future research.Comment: Accepted by IJC
Pattern recognition using inverse resonance filtration
An approach to textures pattern recognition based on inverse resonance
filtration (IRF) is considered. A set of principal resonance harmonics of
textured image signal fluctuations eigen harmonic decomposition (EHD) is used
for the IRF design. It was shown that EHD is invariant to textured image linear
shift. The recognition of texture is made by transfer of its signal into
unstructured signal which simple statistical parameters can be used for texture
pattern recognition. Anomalous variations of this signal point on foreign
objects. Two methods of 2D EHD parameters estimation are considered with the
account of texture signal breaks presence. The first method is based on the
linear symmetry model that is not sensitive to signal phase jumps. The
condition of characteristic polynomial symmetry provides the model stationarity
and periodicity. Second method is based on the eigenvalues problem of matrices
pencil projection into principal vectors space of singular values decomposition
(SVD) of 2D correlation matrix. Two methods of classification of retrieval from
textured image foreign objects are offered.Comment: 8 pages, 3 figure
Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrieval
In this paper, a new texture descriptor based on the local neighborhood
intensity difference is proposed for content based image retrieval (CBIR). For
computation of texture features like Local Binary Pattern (LBP), the center
pixel in a 3*3 window of an image is compared with all the remaining neighbors,
one pixel at a time to generate a binary bit pattern. It ignores the effect of
the adjacent neighbors of a particular pixel for its binary encoding and also
for texture description. The proposed method is based on the concept that
neighbors of a particular pixel hold a significant amount of texture
information that can be considered for efficient texture representation for
CBIR. Taking this into account, we develop a new texture descriptor, named as
Local Neighborhood Intensity Pattern (LNIP) which considers the relative
intensity difference between a particular pixel and the center pixel by
considering its adjacent neighbors and generate a sign and a magnitude pattern.
Since sign and magnitude patterns hold complementary information to each other,
these two patterns are concatenated into a single feature descriptor to
generate a more concrete and useful feature descriptor. The proposed descriptor
has been tested for image retrieval on four databases, including three texture
image databases - Brodatz texture image database, MIT VisTex database and
Salzburg texture database and one face database AT&T face database. The
precision and recall values observed on these databases are compared with some
state-of-art local patterns. The proposed method showed a significant
improvement over many other existing methods.Comment: Expert Systems with Applications(Elsevier
A Novel Feature Descriptor for Image Retrieval by Combining Modified Color Histogram and Diagonally Symmetric Co-occurrence Texture Pattern
In this paper, we have proposed a novel feature descriptors combining color
and texture information collectively. In our proposed color descriptor
component, the inter-channel relationship between Hue (H) and Saturation (S)
channels in the HSV color space has been explored which was not done earlier.
We have quantized the H channel into a number of bins and performed the voting
with saturation values and vice versa by following a principle similar to that
of the HOG descriptor, where orientation of the gradient is quantized into a
certain number of bins and voting is done with gradient magnitude. This helps
us to study the nature of variation of saturation with variation in Hue and
nature of variation of Hue with the variation in saturation. The texture
component of our descriptor considers the co-occurrence relationship between
the pixels symmetric about both the diagonals of a 3x3 window. Our work is
inspired from the work done by Dubey et al.[1]. These two components, viz.
color and texture information individually perform better than existing texture
and color descriptors. Moreover, when concatenated the proposed descriptors
provide significant improvement over existing descriptors for content base
color image retrieval. The proposed descriptor has been tested for image
retrieval on five databases, including texture image databases - MIT VisTex
database and Salzburg texture database and natural scene databases Corel 1K,
Corel 5K and Corel 10K. The precision and recall values experimented on these
databases are compared with some state-of-art local patterns. The proposed
method provided satisfactory results from the experiments.Comment: Preprint Submitte
Efficient Region-Based Image Querying
Retrieving images from large and varied repositories using visual contents
has been one of major research items, but a challenging task in the image
management community. In this paper we present an efficient approach for
region-based image classification and retrieval using a fast multi-level neural
network model. The advantages of this neural model in image classification and
retrieval domain will be highlighted. The proposed approach accomplishes its
goal in three main steps. First, with the help of a mean-shift based
segmentation algorithm, significant regions of the image are isolated.
Secondly, color and texture features of each region are extracted by using
color moments and 2D wavelets decomposition technique. Thirdly the multi-level
neural classifier is trained in order to classify each region in a given image
into one of five predefined categories, i.e., "Sky", "Building", "SandnRock",
"Grass" and "Water". Simulation results show that the proposed method is
promising in terms of classification and retrieval accuracy results. These
results compare favorably with the best published results obtained by other
state-of-the-art image retrieval techniques.Comment: IEEE Publication Format,
https://sites.google.com/site/journalofcomputing
An Overview of the Research on Texture Based Plant Leaf Classification
Plant classification has a broad application prospective in agriculture and
medicine, and is especially significant to the biology diversity research. As
plants are vitally important for environmental protection, it is more important
to identify and classify them accurately. Plant leaf classification is a
technique where leaf is classified based on its different morphological
features. The goal of this paper is to provide an overview of different aspects
of texture based plant leaf classification and related things. At last we will
be concluding about the efficient method i.e. the method that gives better
performance compared to the other methods.Comment: 12 pages,5 figures and 3 table
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