A Meta-Top-Down Method for Large-Scale Hierarchical Classification
ABSTRACT
With
an increasing number of images that are available in social media, image
annotation has emerged as an important research topic due to its application in
image matching and retrieval. Most studies cast image annotation into a
multi-label classification problem. The main shortcoming of this approach is
that it requires a large number of training images with clean and complete
annotations in order to learn a reliable model for tag prediction. We address
this limitation by developing a novel approach that combines the strength of
tag ranking with the power of matrix recovery. Instead of having to make a
binary decision for each tag, our approach ranks tags in the descending order
of their relevance to the given image, significantly simplifying the problem.
In addition, the proposed method aggregates the prediction models for different
tags into a matrix, and casts tag ranking into a matrix recovery problem. It
introduces the matrix trace norm to explicitly control the model complexity so
that a reliable prediction model can be learned for tag ranking even when the
tag space is large and the number of training images is limited. Experiments on
multiple well-known image datasets demonstrate the effectiveness of the
proposed framework for tag ranking compared to the state-ofthe- art approaches
for image annotation and tag ranking.
.
EXISTING SYSTEM
cast
image annotation into a multi-label classification problem. The main
shortcoming of this approach is that it requires a large number of training
images with clean and complete annotations in order to learn a reliable model
for tag prediction. We address this limitation by developing a novel approach
that combines the strength of tag ranking with the power of matrix recovery.
Instead of having to make a binary decision for each tag, our approach ranks
tags in the descending order of their relevance to the given image,
significantly simplifying the problem
PROPOSED SYSTEM:
the proposed method aggregates the
prediction models for different tags into a matrix, and casts tag ranking into
a matrix recovery problem. It introduces the matrix trace norm to explicitly
control the model complexity so that a reliable prediction model can be learned
for tag ranking even when the tag space is large and the number of training
images is limited. Experiments on multiple well-known image datasets
demonstrate the effectiveness of the proposed framework for tag ranking
compared to the state-ofthe- art approaches for image annotation and tag
ranking.
MODULE DESCRIPTION:
Automatic image annotation.
Tag ranking.
Low-rank.
Matrix recovery.
Trace norm.
Automatic image Annotation.
Automatic image annotation aims to
find a subset of keywords/ tags that describes the visual content of an image.
It plays an important role in bridging the semantic gap between low-level
features and high-level semantic content of images. Most automatic image
annotation algorithms can be classified into three categories generative models
that model the joint distribution between tags and visual features,
discriminative models that view image annotation as a classification problem,
and search based approaches. Below, we will briefly review approaches in each
category. Both mixture models and topic models, two well known approaches in
generative model, have been successfully applied to automatic image annotation.
In a Gaussian mixture model is used to model the dependence between keywords
and visual features. In kernel density estimation is applied to model the
distribution of visual features and to estimate the conditional probability of
keyword assignments given the visual features. Topic models annotate images as
samples from a specific mixture of topics, which each topic is a joint
distribution between image features and annotation keywords. Various topic
models have been developed for image annotation, including probabilistic latent
semantic analysis (pLSA) ,latent Dirichlet allocation and hierarchical
Dirichlet processes . Since a large number of training examples are needed for
estimating the joint probability distribution over both features and keywords,
the generative models are unable to handle the challenge of large tag space
with limited number of training images
Discriminative models , views image
annotation as a multi-class classification problem, and learns one binary
classification model for either one or multiple tags. A 2D multiresolution
hidded Markov model (MHMM) is proposed to model the relationship between tags
and visual content .A structured max-margin algorithm is developed in to
exploit the dependence among tags. One problem with discriminative approaches
for image annotation is imbalanced data distribution because each binary
classifier is designed to distinguish image of one class from images of the
other classes. It becomes more severe when the number of classes/tags is large
.Another limitation of these approaches is that they are unable to capture the
correlation among classes, which is known to be important in multi-label
learning. To overcome
Tag ranking.
Tag
ranking aims to learn a ranking function that puts relevant tags in front of
the irrelevant ones. In the simplest form, it learns a scoring function that
assigns larger values to the relevant tags than to those irrelevant ones. In ,
the authors develop a classification framework for tag ranking that computes
tag scores for a test image based on the neighbor voting. It was extended in
[46] to the case where each image is represented by multiple sets of visual
features. Liu et al. utilizes the Kernel
Density Estimation (KDE) to calculate relevance scores for different tags, and
performs a randomwalk to further improve the performance of tag ranking by
exploring the correlation between tags. Similarly, Tang et al. proposed a two-stage graph-based relevance
propagation approach. In , a two-view tag weighting method is proposed to
effectively exploit both the correlation among tags and the dependence between
visual features and tags. In , a max-margin riffled independence model is
developed for tag ranking. As mentioned in the introduction section, most of
the existing algorithms for tag ranking tend to perform poorly when the tag space
is large and the number of training images is limited.
Low-rank.
In mathematics,
low-rankapproximation is a minimization problem, in which the cost function measures
the fit between a given matrix (the data) and an approximating matrix (the
optimization variable), subject to a constraint that the approximating matrix
has reduced rank. The problem is used for mathematical modeling and data compression.
The rank constraint is related to a constraint on the complexity of a model
that fits the data. In applications, often there are other constraints on the
approximating matrix apart from the rank constraint, e.g., non-negativity and Hankel structure.
We study
the rank, trace-norm and max-norm as complexity measures of matrices, focusing
on the problem of fitting a matrix with matrices having low complexity. We
present generalization error bounds for predicting unobserved entries that are
based on these measures. We also consider the possible relations between these
measures. We show gaps between them, and bounds on the extent of such gaps.
Matrix recovery.
A common modeling assumption in many
engineering applications is that the underlying data lies (approximately) on a
low-dimensional linear subspace. This property has been widely exploited by
classical Principal Component Analysis (PCA) to achieve dimensionality
reduction. However, real-life data is often corrupted with large errors or can
even be incomplete. Although classical PCA is effective against the presence of
small Gaussian noise in the data, it is highly sensitive to even sparse errors
of very high magnitude. We propose
powerful tools that exactly and efficiently correct large errors in such
structured data. The basic idea is to formulate the problem as a matrix rank
minimization problem and solve it
efficiently by nuclear-norm minimization. Our algorithms achieve
state-of-the-art performance in low-rank matrix recovery with theoretical guarantees.
Please browse the links to the left for more information. The introduction
section provides a brief overview of the low-rank matrix recovery problem and
introduces state-of-the-art algorithms to solve. Please refer to our papers in
the references section for complete technical details, and to the sample code
section for MATLAB packages. The applications section showcases engineering
problems where our techniques have been used to achieve state-of-the-art
performance.
Trace norm.
Trace-norm
and max-norm as complexity measures of matrices, focusing on the problem of
fitting a matrix with matrices having low complexity. We present generalization
error bounds for predicting unobserved entries that are based on these
measures. We also consider the possible relations between these measures
Accelerated Gradient Algorithm
Gradient descent is a first-order optimization algorithm. To
find a local minimum of a function using gradient descent, one takes
steps proportional to the negative of the gradient (or
of the approximate gradient) of the function at the current point. If instead
one takes steps proportional to the positive of the gradient, one
approaches a local maximum of that function; the procedure is then known
as gradient ascent.
Gradient descent is also known
as steepest descent, or the method of steepest descent. When known as
the latter, gradient descent should not be confused with the method of steepest descent for approximating integrals.
System Configuration:
HARDWARE REQUIREMENTS:
Hardware - Pentium
Speed - 1.1 GHz
RAM - 1GB
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
SOFTWARE REQUIREMENTS:
Operating System : Windows
Technology : Java and J2EE
Web Technologies : Html, JavaScript, CSS
IDE : My Eclipse
Web Server : Tomcat
Tool kit : Android
Phone
Database : My SQL
Java Version :
J2SDK1.5
CONCLUSION
Extensive experiments on image
annotation and tag ranking have demonstrated that the proposed method
significantly outperforms several state-of-the-art methods for image annotation
especially when the number of training images is limited and when many of the
assigned image tags are missing. In the future, we plan to apply the proposed
framework to the image annotation problem when image tags are acquired by
crowdsouring that tend to be noisy and incomplete.
In addition, the proposed method
aggregates the prediction models for different tags into a matrix, and casts
tag ranking into a matrix recovery problem. It introduces the matrix trace norm
to explicitly control the model complexity so that a reliable prediction model
can be learned for tag ranking even when the tag space is large and the number
of training images is limited. Experiments on multiple well-known image
datasets demonstrate the effectiveness of the proposed framework for tag
ranking compared to the state-ofthe- art approaches for image annotation and
tag ranking.
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