2,911 research outputs found
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
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
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
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
Fractional Local Neighborhood Intensity Pattern for Image Retrieval using Genetic Algorithm
In this paper, a new texture descriptor named "Fractional Local Neighborhood
Intensity Pattern" (FLNIP) has been proposed for content based image retrieval
(CBIR). It is an extension of the Local Neighborhood Intensity Pattern
(LNIP)[1]. FLNIP calculates the relative intensity difference between a
particular pixel and the center pixel of a 3x3 window by considering the
relationship with adjacent neighbors. In this work, the fractional change in
the local neighborhood involving the adjacent neighbors has been calculated
first with respect to one of the eight neighbors of the center pixel of a 3x3
window. Next, the fractional change has been calculated with respect to the
center itself. The two values of fractional change are next compared to
generate a binary bit pattern. Both sign and magnitude information are encoded
in a single descriptor as it deals with the relative change in magnitude in the
adjacent neighborhood i.e., the comparison of the fractional change. The
descriptor is applied on four multi-resolution images -- one being the raw
image and the other three being filtered gaussian images obtained by applying
gaussian filters of different standard deviations on the raw image to signify
the importance of exploring texture information at different resolutions in an
image. The four sets of distances obtained between the query and the target
image are then combined with a genetic algorithm based approach to improve the
retrieval performance by minimizing the distance between similar class images.
The performance of the method has been tested for image retrieval on four
popular databases. The precision and recall values observed on these databases
have been compared with recent state-of-art local patterns. The proposed method
has shown a significant improvement over many other existing methods.Comment: MTAP, Springer(Minor Revision
Content-Based Video Browsing by Text Region Localization and Classification
The amount of digital video data is increasing over the world. It highlights
the need for efficient algorithms that can index, retrieve and browse this data
by content. This can be achieved by identifying semantic description captured
automatically from video structure. Among these descriptions, text within video
is considered as rich features that enable a good way for video indexing and
browsing. Unlike most video text detection and extraction methods that treat
video sequences as collections of still images, we propose in this paper
spatiotemporal. video-text localization and identification approach which
proceeds in two main steps: text region localization and text region
classification. In the first step we detect the significant appearance of the
new objects in a frame by a split and merge processes applied on binarized edge
frame pair differences. Detected objects are, a priori, considered as text.
They are then filtered according to both local contrast variation and texture
criteria in order to get the effective ones. The resulted text regions are
classified based on a visual grammar descriptor containing a set of semantic
text class regions characterized by visual features. A visual table of content
is then generated based on extracted text regions occurring within video
sequence enriched by a semantic identification. The experimentation performed
on a variety of video sequences shows the efficiency of our approach.Comment: 11 pages, 12 figures, International Journal of Video & Image
Processing and Network Security IJVIPNS-IJENS Vol:10 No: 0
LDOP: Local Directional Order Pattern for Robust Face Retrieval
The local descriptors have gained wide range of attention due to their
enhanced discriminative abilities. It has been proved that the consideration of
multi-scale local neighborhood improves the performance of the descriptor,
though at the cost of increased dimension. This paper proposes a novel method
to construct a local descriptor using multi-scale neighborhood by finding the
local directional order among the intensity values at different scales in a
particular direction. Local directional order is the multi-radius relationship
factor in a particular direction. The proposed local directional order pattern
(LDOP) for a particular pixel is computed by finding the relationship between
the center pixel and local directional order indexes. It is required to
transform the center value into the range of neighboring orders. Finally, the
histogram of LDOP is computed over whole image to construct the descriptor. In
contrast to the state-of-the-art descriptors, the dimension of the proposed
descriptor does not depend upon the number of neighbors involved to compute the
order; it only depends upon the number of directions. The introduced descriptor
is evaluated over the image retrieval framework and compared with the
state-of-the-art descriptors over challenging face databases such as PaSC, LFW,
PubFig, FERET, AR, AT&T, and ExtendedYale. The experimental results confirm the
superiority and robustness of the LDOP descriptor.Comment: Published in Multimedia Tools and Applications, Springe
Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy
This paper attempts to provide the reader a place to begin studying the
application of computer vision and machine learning to gastrointestinal (GI)
endoscopy. They have been classified into 18 categories. It should be be noted
by the reader that this is a review from pre-deep learning era. A lot of deep
learning based applications have not been covered in this thesis
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
Performance evaluation of wavelet scattering network in image texture classification in various color spaces
Texture plays an important role in many image analysis applications. In this
paper, we give a performance evaluation of color texture classification by
performing wavelet scattering network in various color spaces. Experimental
results on the KTH_TIPS_COL database show that opponent RGB based wavelet
scattering network outperforms other color spaces. Therefore, when dealing with
the problem of color texture classification, opponent RGB based wavelet
scattering network is recommended.Comment: 6 pages, 4 figures, 2 table
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