19,192 research outputs found
Edge direction matrixes-based local binar patterns descriptor for shape pattern recognition
Shapes and texture image recognition usage is an essential branch of pattern
recognition. It is made up of techniques that aim at extracting information
from images via human knowledge and works. Local Binary Pattern (LBP) ensures
encoding global and local information and scaling invariance by introducing a
look-up table to reflect the uniformity structure of an object. However, edge
direction matrixes (EDMS) only apply global invariant descriptor which employs
first and secondary order relationships. The main idea behind this methodology
is the need of improved recognition capabilities, a goal achieved by the
combinative use of these descriptors. This collaboration aims to make use of
the major advantages each one presents, by simultaneously complementing each
other, in order to elevate their weak points. By using multiple classifier
approaches such as random forest and multi-layer perceptron neural network, the
proposed combinative descriptor are compared with the state of the art
combinative methods based on Gray-Level Co-occurrence matrix (GLCM with EDMS),
LBP and moment invariant on four benchmark dataset MPEG-7 CE-Shape-1, KTH-TIPS
image, Enghlishfnt and Arabic calligraphy . The experiments have shown the
superiority of the introduced descriptor over the GLCM with EDMS, LBP and
moment invariants and other well-known descriptor such as Scale Invariant
Feature Transform from the literature
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
Iris Recognition Based on LBP and Combined LVQ Classifier
Iris recognition is considered as one of the best biometric methods used for
human identification and verification, this is because of its unique features
that differ from one person to another, and its importance in the security
field. This paper proposes an algorithm for iris recognition and classification
using a system based on Local Binary Pattern and histogram properties as a
statistical approaches for feature extraction, and Combined Learning Vector
Quantization Classifier as Neural Network approach for classification, in order
to build a hybrid model depends on both features. The localization and
segmentation techniques are presented using both Canny edge detection and Hough
Circular Transform in order to isolate an iris from the whole eye image and for
noise detection .Feature vectors results from LBP is applied to a Combined LVQ
classifier with different classes to determine the minimum acceptable
performance, and the result is based on majority voting among several LVQ
classifier. Different iris datasets CASIA, MMU1, MMU2, and LEI with different
extensions and size are presented. Since LBP is working on a grayscale level so
colored iris images should be transformed into a grayscale level. The proposed
system gives a high recognition rate 99.87 % on different iris datasets
compared with other methods.Comment: 12 Pages, 12 Figure
HEp-2 Cell Classification via Fusing Texture and Shape Information
Indirect Immunofluorescence (IIF) HEp-2 cell image is an effective evidence
for diagnosis of autoimmune diseases. Recently computer-aided diagnosis of
autoimmune diseases by IIF HEp-2 cell classification has attracted great
attention. However the HEp-2 cell classification task is quite challenging due
to large intra-class variation and small between-class variation. In this paper
we propose an effective and efficient approach for the automatic classification
of IIF HEp-2 cell image by fusing multi-resolution texture information and
richer shape information. To be specific, we propose to: a) capture the
multi-resolution texture information by a novel Pairwise Rotation Invariant
Co-occurrence of Local Gabor Binary Pattern (PRICoLGBP) descriptor, b) depict
the richer shape information by using an Improved Fisher Vector (IFV) model
with RootSIFT features which are sampled from large image patches in multiple
scales, and c) combine them properly. We evaluate systematically the proposed
approach on the IEEE International Conference on Pattern Recognition (ICPR)
2012, IEEE International Conference on Image Processing (ICIP) 2013 and ICPR
2014 contest data sets. The experimental results for the proposed methods
significantly outperform the winners of ICPR 2012 and ICIP 2013 contest, and
achieve comparable performance with the winner of the newly released ICPR 2014
contest.Comment: 11 pages, 7 figure
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
LGLG-WPCA: An Effective Texture-based Method for Face Recognition
In this paper, we proposed an effective face feature extraction method by
Learning Gabor Log-Euclidean Gaussian with Whitening Principal Component
Analysis (WPCA), called LGLG-WPCA. The proposed method learns face features
from the embedded multivariate Gaussian in Gabor wavelet domain; it has the
robust performance to adverse conditions such as varying poses, skin aging and
uneven illumination. Because the space of Gaussian is a Riemannian manifold and
it is difficult to incorporate learning mechanism in the model. To address this
issue, we use L2EMG to map the multidimensional Gaussian model to the linear
space, and then use WPCA to learn face features. We also implemented the
key-point-based version of LGLG-WPCA, called LGLG(KP)-WPCA. Experiments show
the proposed methods are effective and promising for face texture feature
extraction and the combination of the feature of the proposed methods and the
features of Deep Convolutional Network (DCNN) achieved the best recognition
accuracies on FERET database compared to the state-of-the-art methods. In the
next version of this paper, we will test the performance of the proposed
methods on the large-varying pose databases
Low-Level Features for Image Retrieval Based on Extraction of Directional Binary Patterns and Its Oriented Gradients Histogram
In this paper, we present a novel approach for image retrieval based on
extraction of low level features using techniques such as Directional Binary
Code, Haar Wavelet transform and Histogram of Oriented Gradients. The DBC
texture descriptor captures the spatial relationship between any pair of
neighbourhood pixels in a local region along a given direction, while Local
Binary Patterns descriptor considers the relationship between a given pixel and
its surrounding neighbours. Therefore, DBC captures more spatial information
than LBP and its variants, also it can extract more edge information than LBP.
Hence, we employ DBC technique in order to extract grey level texture feature
from each RGB channels individually and computed texture maps are further
combined which represents colour texture features of an image. Then, we
decomposed the extracted colour texture map and original image using Haar
wavelet transform. Finally, we encode the shape and local features of wavelet
transformed images using Histogram of Oriented Gradients for content based
image retrieval. The performance of proposed method is compared with existing
methods on two databases such as Wang's corel image and Caltech 256. The
evaluation results show that our approach outperforms the existing methods for
image retrieval.Comment: 7 Figures, 5 Tables 16 Pages in Computer Applications: An
International Journal (CAIJ), Vol.2, No.1, February 201
An Approach to the Analysis of the South Slavic Medieval Labels Using Image Texture
The paper presents a new script classification method for the discrimination
of the South Slavic medieval labels. It consists in the textural analysis of
the script types. In the first step, each letter is coded by the equivalent
script type, which is defined by its typographical features. Obtained coded
text is subjected to the run-length statistical analysis and to the adjacent
local binary pattern analysis in order to extract the features. The result
shows a diversity between the extracted features of the scripts, which makes
the feature classification more effective. It is the basis for the
classification process of the script identification by using an extension of a
state-of-the-art approach for document clustering. The proposed method is
evaluated on an example of hand-engraved in stone and hand-printed in paper
labels in old Cyrillic, angular and round Glagolitic. Experiments demonstrate
very positive results, which prove the effectiveness of the proposed method.Comment: 15 pages, 9 figures, 3rd Workshop on Recognition and Action for Scene
Understanding (REACTS 2015
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
A PCA-Based Convolutional Network
In this paper, we propose a novel unsupervised deep learning model, called
PCA-based Convolutional Network (PCN). The architecture of PCN is composed of
several feature extraction stages and a nonlinear output stage. Particularly,
each feature extraction stage includes two layers: a convolutional layer and a
feature pooling layer. In the convolutional layer, the filter banks are simply
learned by PCA. In the nonlinear output stage, binary hashing is applied. For
the higher convolutional layers, the filter banks are learned from the feature
maps that were obtained in the previous stage. To test PCN, we conducted
extensive experiments on some challenging tasks, including handwritten digits
recognition, face recognition and texture classification. The results show that
PCN performs competitive with or even better than state-of-the-art deep
learning models. More importantly, since there is no back propagation for
supervised finetuning, PCN is much more efficient than existing deep networks.Comment: 8 pages,5 figure
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