19,792 research outputs found
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
A Survey on Object Detection in Optical Remote Sensing Images
Object detection in optical remote sensing images, being a fundamental but
challenging problem in the field of aerial and satellite image analysis, plays
an important role for a wide range of applications and is receiving significant
attention in recent years. While enormous methods exist, a deep review of the
literature concerning generic object detection is still lacking. This paper
aims to provide a review of the recent progress in this field. Different from
several previously published surveys that focus on a specific object class such
as building and road, we concentrate on more generic object categories
including, but are not limited to, road, building, tree, vehicle, ship,
airport, urban-area. Covering about 270 publications we survey 1) template
matching-based object detection methods, 2) knowledge-based object detection
methods, 3) object-based image analysis (OBIA)-based object detection methods,
4) machine learning-based object detection methods, and 5) five publicly
available datasets and three standard evaluation metrics. We also discuss the
challenges of current studies and propose two promising research directions,
namely deep learning-based feature representation and weakly supervised
learning-based geospatial object detection. It is our hope that this survey
will be beneficial for the researchers to have better understanding of this
research field.Comment: This manuscript is the accepted version for ISPRS Journal of
Photogrammetry and Remote Sensin
Affine-Gradient Based Local Binary Pattern Descriptor for Texture Classiffication
We present a novel Affine-Gradient based Local Binary Pattern (AGLBP)
descriptor for texture classification. It is very hard to describe complicated
texture using single type information, such as Local Binary Pattern (LBP),
which just utilizes the sign information of the difference between the pixel
and its local neighbors. Our descriptor has three characteristics: 1) In order
to make full use of the information contained in the texture, the
Affine-Gradient, which is different from Euclidean-Gradient and invariant to
affine transformation is incorporated into AGLBP. 2) An improved method is
proposed for rotation invariance, which depends on the reference direction
calculating respect to local neighbors. 3) Feature selection method,
considering both the statistical frequency and the intraclass variance of the
training dataset, is also applied to reduce the dimensionality of descriptors.
Experiments on three standard texture datasets, Outex12, Outex10 and KTH-TIPS2,
are conducted to evaluate the performance of AGLBP. The results show that our
proposed descriptor gets better performance comparing to some state-of-the-art
rotation texture descriptors in texture classification.Comment: 11 pages,4 page
LATCH: Learned Arrangements of Three Patch Codes
We present a novel means of describing local image appearances using binary
strings. Binary descriptors have drawn increasing interest in recent years due
to their speed and low memory footprint. A known shortcoming of these
representations is their inferior performance compared to larger, histogram
based descriptors such as the SIFT. Our goal is to close this performance gap
while maintaining the benefits attributed to binary representations. To this
end we propose the Learned Arrangements of Three Patch Codes descriptors, or
LATCH. Our key observation is that existing binary descriptors are at an
increased risk from noise and local appearance variations. This, as they
compare the values of pixel pairs; changes to either of the pixels can easily
lead to changes in descriptor values, hence damaging its performance. In order
to provide more robustness, we instead propose a novel means of comparing pixel
patches. This ostensibly small change, requires a substantial redesign of the
descriptors themselves and how they are produced. Our resulting LATCH
representation is rigorously compared to state-of-the-art binary descriptors
and shown to provide far better performance for similar computation and space
requirements
Multi-Level Feature Descriptor for Robust Texture Classification via Locality-Constrained Collaborative Strategy
This paper introduces a simple but highly efficient ensemble for robust
texture classification, which can effectively deal with translation, scale and
changes of significant viewpoint problems. The proposed method first inherits
the spirit of spatial pyramid matching model (SPM), which is popular for
encoding spatial distribution of local features, but in a flexible way,
partitioning the original image into different levels and incorporating
different overlapping patterns of each level. This flexible setup helps capture
the informative features and produces sufficient local feature codes by some
well-chosen aggregation statistics or pooling operations within each
partitioned region, even when only a few sample images are available for
training. Then each texture image is represented by several orderless feature
codes and thereby all the training data form a reliable feature pond. Finally,
to take full advantage of this feature pond, we develop a collaborative
representation-based strategy with locality constraint (LC-CRC) for the final
classification, and experimental results on three well-known public texture
datasets demonstrate the proposed approach is very competitive and even
outperforms several state-of-the-art methods. Particularly, when only a few
samples of each category are available for training, our approach still
achieves very high classification performance
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
Texture image analysis and texture classification methods - A review
Tactile texture refers to the tangible feel of a surface and visual texture
refers to see the shape or contents of the image. In the image processing, the
texture can be defined as a function of spatial variation of the brightness
intensity of the pixels. Texture is the main term used to define objects or
concepts of a given image. Texture analysis plays an important role in computer
vision cases such as object recognition, surface defect detection, pattern
recognition, medical image analysis, etc. Since now many approaches have been
proposed to describe texture images accurately. Texture analysis methods
usually are classified into four categories: statistical methods, structural,
model-based and transform-based methods. This paper discusses the various
methods used for texture or analysis in details. New researches shows the power
of combinational methods for texture analysis, which can't be in specific
category. This paper provides a review on well known combinational methods in a
specific section with details. This paper counts advantages and disadvantages
of well-known texture image descriptors in the result part. Main focus in all
of the survived methods is on discrimination performance, computational
complexity and resistance to challenges such as noise, rotation, etc. A brief
review is also made on the common classifiers used for texture image
classification. Also, a survey on texture image benchmark datasets is included.Comment: 29 Pages, Keywords: Texture Image, Texture Analysis, Texture
classification, Feature extraction, Image processing, Local Binary Patterns,
Benchmark texture image dataset
Ensemble of Deep Learned Features for Melanoma Classification
The aim of this work is to propose an ensemble of descriptors for Melanoma
Classification, whose performance has been evaluated on validation and test
datasets of the melanoma challenge 2018. The system proposed here achieves a
strong discriminative power thanks to the combination of multiple descriptors.
The proposed system represents a very simple yet effective way of boosting the
performance of trained CNNs by composing multiple CNNs into an ensemble and
combining scores by sum rule. Several types of ensembles are considered, with
different CNN architectures along with different learning parameter sets.
Moreover CNN are used as feature extractors: an input image is processed by a
trained CNN and the response of a particular layer (usually the classification
layer, but also internal layers can be employed) is treated as a descriptor for
the image and used for training a set of Support Vector Machines (SVM)
Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors
One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce
the semantic gaps between low-level features and high-level semantic concepts.
In CBIR, the images are represented in the feature space and the performance of
CBIR depends on the type of selected feature representation. Late fusion also
known as visual words integration is applied to enhance the performance of
image retrieval. The recent advances in image retrieval diverted the focus of
research towards the use of binary descriptors as they are reported
computationally efficient. In this paper, we aim to investigate the late fusion
of Fast Retina Keypoint (FREAK) and Scale Invariant Feature Transform (SIFT).
The late fusion of binary and local descriptor is selected because among binary
descriptors, FREAK has shown good results in classification-based problems
while SIFT is robust to translation, scaling, rotation and small distortions.
The late fusion of FREAK and SIFT integrates the performance of both feature
descriptors for an effective image retrieval. Experimental results and
comparisons show that the proposed late fusion enhances the performances of
image retrieval
Improving LBP and its variants using anisotropic diffusion
The main purpose of this paper is to propose a new preprocessing step in
order to improve local feature descriptors and texture classification.
Preprocessing is implemented by using transformations which help highlight
salient features that play a significant role in texture recognition. We
evaluate and compare four different competing methods: three different
anisotropic diffusion methods including the classical anisotropic Perona-Malik
diffusion and two subsequent regularizations of it and the application of a
Gaussian kernel, which is the classical multiscale approach in texture
analysis. The combination of the transformed images and the original ones are
analyzed. The results show that the use of the preprocessing step does lead to
improved texture recognition.Comment: 14 pages, 10 figure
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