102,301 research outputs found
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
Local Multi-Grouped Binary Descriptor with Ring-based Pooling Configuration and Optimization
Local binary descriptors are attracting increasingly attention due to their
great advantages in computational speed, which are able to achieve real-time
performance in numerous image/vision applications. Various methods have been
proposed to learn data-dependent binary descriptors. However, most existing
binary descriptors aim overly at computational simplicity at the expense of
significant information loss which causes ambiguity in similarity measure using
Hamming distance. In this paper, by considering multiple features might share
complementary information, we present a novel local binary descriptor, referred
as Ring-based Multi-Grouped Descriptor (RMGD), to successfully bridge the
performance gap between current binary and floated-point descriptors. Our
contributions are two-fold. Firstly, we introduce a new pooling configuration
based on spatial ring-region sampling, allowing for involving binary tests on
the full set of pairwise regions with different shapes, scales and distances.
This leads to a more meaningful description than existing methods which
normally apply a limited set of pooling configurations. Then, an extended
Adaboost is proposed for efficient bit selection by emphasizing high variance
and low correlation, achieving a highly compact representation. Secondly, the
RMGD is computed from multiple image properties where binary strings are
extracted. We cast multi-grouped features integration as rankSVM or sparse SVM
learning problem, so that different features can compensate strongly for each
other, which is the key to discriminativeness and robustness. The performance
of RMGD was evaluated on a number of publicly available benchmarks, where the
RMGD outperforms the state-of-the-art binary descriptors significantly.Comment: To appear in IEEE Trans. on Image Processing, 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
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
Enhancing the retrieval performance by combing the texture and edge features
In this paper, anew algorithm which is based on geometrical moments and local
binary patterns (LBP) for content based image retrieval (CBIR) is proposed. In
geometrical moments, each vector is compared with the all other vectors for
edge map generation. The same concept is utilized at LBP calculation which is
generating nine LBP patterns from a given 3x3 pattern. Finally, nine LBP
histograms are calculated which are used as a feature vector for image
retrieval. Moments are important features used in recognition of different
types of images. Two experiments have been carried out for proving the worth of
our algorithm. The results after being investigated shows a significant
improvement in terms of their evaluation measures as compared to LBP and other
existing transform domain techniques.Comment: 7 pages,8 figures, one tabl
From handcrafted to deep local features
This paper presents an overview of the evolution of local features from
handcrafted to deep-learning-based methods, followed by a discussion of several
benchmarks and papers evaluating such local features. Our investigations are
motivated by 3D reconstruction problems, where the precise location of the
features is important. As we describe these methods, we highlight and explain
the challenges of feature extraction and potential ways to overcome them. We
first present handcrafted methods, followed by methods based on classical
machine learning and finally we discuss methods based on deep-learning. This
largely chronologically-ordered presentation will help the reader to fully
understand the topic of image and region description in order to make best use
of it in modern computer vision applications. In particular, understanding
handcrafted methods and their motivation can help to understand modern
approaches and how machine learning is used to improve the results. We also
provide references to most of the relevant literature and code.Comment: Preprin
LOAD: Local Orientation Adaptive Descriptor for Texture and Material Classification
In this paper, we propose a novel local feature, called Local Orientation
Adaptive Descriptor (LOAD), to capture regional texture in an image. In LOAD,
we proposed to define point description on an Adaptive Coordinate System (ACS),
adopt a binary sequence descriptor to capture relationships between one point
and its neighbors and use multi-scale strategy to enhance the discriminative
power of the descriptor. The proposed LOAD enjoys not only discriminative power
to capture the texture information, but also has strong robustness to
illumination variation and image rotation. Extensive experiments on benchmark
data sets of texture classification and real-world material recognition show
that the proposed LOAD yields the state-of-the-art performance. It is worth to
mention that we achieve a 65.4\% classification accuracy-- which is, to the
best of our knowledge, the highest record by far --on Flickr Material Database
by using a single feature. Moreover, by combining LOAD with the feature
extracted by Convolutional Neural Networks (CNN), we obtain significantly
better performance than both the LOAD and CNN. This result confirms that the
LOAD is complementary to the learning-based features.Comment: 13 pages, 7 figure
Role of Class-specific Features in Various Classification Frameworks for Human Epithelial (HEp-2) Cell Images
The antinuclear antibody detection with human epithelial cells is a popular
approach for autoimmune diseases diagnosis. The manual evaluation demands time,
effort and capital, and automation in screening can greatly aid the physicians
in these respects. In this work, we employ simple, efficient and visually more
interpretable, class-specific features which defined based on the visual
characteristics of each class. We believe that defining features with a good
visual interpretation, is indeed important in a scenario, where such an
approach is used in an interactive CAD system for pathologists. Considering
that problem consists of few classes, and our rather simplistic feature
definitions, frameworks can be structured as hierarchies of various binary
classifiers. These variants include frameworks which are earlier explored and
some which are not explored for this task. We perform various experiments which
include traditional texture features and demonstrate the effectiveness of
class-specific features in various frameworks. We make insightful comparisons
between different types of classification frameworks given their silent aspects
and pros and cons over each other. We also demonstrate an experiment with only
intermediates samples for testing. The proposed work yields encouraging results
with respect to the state-of-the-art and highlights the role of class-specific
features in different classification frameworks
PCANet-II: When PCANet Meets the Second Order Pooling
PCANet, as one noticeable shallow network, employs the histogram
representation for feature pooling. However, there are three main problems
about this kind of pooling method. First, the histogram-based pooling method
binarizes the feature maps and leads to inevitable discriminative information
loss. Second, it is difficult to effectively combine other visual cues into a
compact representation, because the simple concatenation of various visual cues
leads to feature representation inefficiency. Third, the dimensionality of
histogram-based output grows exponentially with the number of feature maps
used. In order to overcome these problems, we propose a novel shallow network
model, named as PCANet-II. Compared with the histogram-based output, the second
order pooling not only provides more discriminative information by preserving
both the magnitude and sign of convolutional responses, but also dramatically
reduces the size of output features. Thus we combine the second order
statistical pooling method with the shallow network, i.e., PCANet. Moreover, it
is easy to combine other discriminative and robust cues by using the second
order pooling. So we introduce the binary feature difference encoding scheme
into our PCANet-II to further improve robustness. Experiments demonstrate the
effectiveness and robustness of our proposed PCANet-II method
A Performance Evaluation of Local Features for Image Based 3D Reconstruction
This paper performs a comprehensive and comparative evaluation of the state
of the art local features for the task of image based 3D reconstruction. The
evaluated local features cover the recently developed ones by using powerful
machine learning techniques and the elaborately designed handcrafted features.
To obtain a comprehensive evaluation, we choose to include both float type
features and binary ones. Meanwhile, two kinds of datasets have been used in
this evaluation. One is a dataset of many different scene types with
groundtruth 3D points, containing images of different scenes captured at fixed
positions, for quantitative performance evaluation of different local features
in the controlled image capturing situations. The other dataset contains
Internet scale image sets of several landmarks with a lot of unrelated images,
which is used for qualitative performance evaluation of different local
features in the free image collection situations. Our experimental results show
that binary features are competent to reconstruct scenes from controlled image
sequences with only a fraction of processing time compared to use float type
features. However, for the case of large scale image set with many distracting
images, float type features show a clear advantage over binary ones
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