18,975 research outputs found
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
Deep Local Binary Patterns
Local Binary Pattern (LBP) is a traditional descriptor for texture analysis
that gained attention in the last decade. Being robust to several properties
such as invariance to illumination translation and scaling, LBPs achieved
state-of-the-art results in several applications. However, LBPs are not able to
capture high-level features from the image, merely encoding features with low
abstraction levels. In this work, we propose Deep LBP, which borrow ideas from
the deep learning community to improve LBP expressiveness. By using
parametrized data-driven LBP, we enable successive applications of the LBP
operators with increasing abstraction levels. We validate the relevance of the
proposed idea in several datasets from a wide range of applications. Deep LBP
improved the performance of traditional and multiscale LBP in all cases
Multichannel Distributed Local Pattern for Content Based Indexing and Retrieval
A novel color feature descriptor, Multichannel Distributed Local Pattern
(MDLP) is proposed in this manuscript. The MDLP combines the salient features
of both local binary and local mesh patterns in the neighborhood. The
multi-distance information computed by the MDLP aids in robust extraction of
the texture arrangement. Further, MDLP features are extracted for each color
channel of an image. The retrieval performance of the MDLP is evaluated on the
three benchmark datasets for CBIR, namely Corel-5000, Corel-10000 and MIT-Color
Vistex respectively. The proposed technique attains substantial improvement as
compared to other state-of- the-art feature descriptors in terms of various
evaluation parameters such as ARP and ARR on the respective databases.Comment: Accepted in INDICON-201
Face Identification using Local Ternary Tree Pattern based Spatial Structural Components
This paper reports a face identification system which makes use of a novel
local descriptor called Local Ternary Tree Pattern (LTTP). Exploiting and
extracting distinctive local descriptor from a face image plays a crucial role
in face identification task in the presence of a variety of face images
including constrained, unconstrained and plastic surgery images. LTTP has been
used to extract robust and useful spatial features which use to describe the
various structural components on a face. To extract the features, a ternary
tree is formed for each pixel with its eight neighbors in each block. LTTP
pattern can be generated in four forms such as LTTP Left Depth (LTTP LD), LTTP
Left Breadth (LTTP LB), LTTP Right Depth (LTTP RD) and LTTP Right Breadth (LTTP
RB). The encoding schemes of these patterns are very simple and efficient in
terms of computational as well as time complexity. The proposed face
identification system is tested on six face databases, namely, the UMIST, the
JAFFE, the extended Yale face B, the Plastic Surgery, the LFW and the UFI. The
experimental evaluation demonstrates the most promising results considering a
variety of faces captured under different environments. The proposed LTTP based
system is also compared with some local descriptors under identical conditions.Comment: 13 pages, 5 figures, conference pape
Human Emotional Facial Expression Recognition
An automatic Facial Expression Recognition (FER) model with Adaboost face
detector, feature selection based on manifold learning and synergetic prototype
based classifier has been proposed. Improved feature selection method and
proposed classifier can achieve favorable effectiveness to performance FER in
reasonable processing time
Robust Face Recognition with Structural Binary Gradient Patterns
This paper presents a computationally efficient yet powerful binary framework
for robust facial representation based on image gradients. It is termed as
structural binary gradient patterns (SBGP). To discover underlying local
structures in the gradient domain, we compute image gradients from multiple
directions and simplify them into a set of binary strings. The SBGP is derived
from certain types of these binary strings that have meaningful local
structures and are capable of resembling fundamental textural information. They
detect micro orientational edges and possess strong orientation and locality
capabilities, thus enabling great discrimination. The SBGP also benefits from
the advantages of the gradient domain and exhibits profound robustness against
illumination variations. The binary strategy realized by pixel correlations in
a small neighborhood substantially simplifies the computational complexity and
achieves extremely efficient processing with only 0.0032s in Matlab for a
typical face image. Furthermore, the discrimination power of the SBGP can be
enhanced on a set of defined orientational image gradient magnitudes, further
enforcing locality and orientation. Results of extensive experiments on various
benchmark databases illustrate significant improvements of the SBGP based
representations over the existing state-of-the-art local descriptors in the
terms of discrimination, robustness and complexity. Codes for the SBGP methods
will be available at
http://www.eee.manchester.ac.uk/research/groups/sisp/software/
New Fuzzy LBP Features for Face Recognition
There are many Local texture features each very in way they implement and
each of the Algorithm trying improve the performance. An attempt is made in
this paper to represent a theoretically very simple and computationally
effective approach for face recognition. In our implementation the face image
is divided into 3x3 sub-regions from which the features are extracted using the
Local Binary Pattern (LBP) over a window, fuzzy membership function and at the
central pixel. The LBP features possess the texture discriminative property and
their computational cost is very low. By utilising the information from LBP,
membership function, and central pixel, the limitations of traditional LBP is
eliminated. The bench mark database like ORL and Sheffield Databases are used
for the evaluation of proposed features with SVM classifier. For the proposed
approach K-fold and ROC curves are obtained and results are compared
Facial expression recognition based on local region specific features and support vector machines
Facial expressions are one of the most powerful, natural and immediate means
for human being to communicate their emotions and intensions. Recognition of
facial expression has many applications including human-computer interaction,
cognitive science, human emotion analysis, personality development etc. In this
paper, we propose a new method for the recognition of facial expressions from
single image frame that uses combination of appearance and geometric features
with support vector machines classification. In general, appearance features
for the recognition of facial expressions are computed by dividing face region
into regular grid (holistic representation). But, in this paper we extracted
region specific appearance features by dividing the whole face region into
domain specific local regions. Geometric features are also extracted from
corresponding domain specific regions. In addition, important local regions are
determined by using incremental search approach which results in the reduction
of feature dimension and improvement in recognition accuracy. The results of
facial expressions recognition using features from domain specific regions are
also compared with the results obtained using holistic representation. The
performance of the proposed facial expression recognition system has been
validated on publicly available extended Cohn-Kanade (CK+) facial expression
data sets.Comment: Facial expressions, Local representation, Appearance features,
Geometric features, Support vector machine
A Survey on Periocular Biometrics Research
Periocular refers to the facial region in the vicinity of the eye, including
eyelids, lashes and eyebrows. While face and irises have been extensively
studied, the periocular region has emerged as a promising trait for
unconstrained biometrics, following demands for increased robustness of face or
iris systems. With a surprisingly high discrimination ability, this region can
be easily obtained with existing setups for face and iris, and the requirement
of user cooperation can be relaxed, thus facilitating the interaction with
biometric systems. It is also available over a wide range of distances even
when the iris texture cannot be reliably obtained (low resolution) or under
partial face occlusion (close distances). Here, we review the state of the art
in periocular biometrics research. A number of aspects are described,
including: i) existing databases, ii) algorithms for periocular detection
and/or segmentation, iii) features employed for recognition, iv) identification
of the most discriminative regions of the periocular area, v) comparison with
iris and face modalities, vi) soft-biometrics (gender/ethnicity
classification), and vii) impact of gender transformation and plastic surgery
on the recognition accuracy. This work is expected to provide an insight of the
most relevant issues in periocular biometrics, giving a comprehensive coverage
of the existing literature and current state of the art.Comment: Published in Pattern Recognition Letter
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