1. User-aware Image Tag Refinement via
Ternary Semantic Analysis
Abstract:
Large-scale user contributed images with tags are easily available on photo
sharing websites. However, the noisy or incomplete correspondence between
the images and tags prohibits them from being leveraged for precise image
retrieval and effective management. To tackle the problem of tag refinement,
we propose a method of Ranking based Multi-correlation Tensor
Factorization (RMTF), to jointly model the ternary relations among user,
image and tag, and further to precisely reconstruct the user-aware image-tag
associations as a result. Since the user interest or background can be
explored to eliminate the ambiguity of image tags, the proposed RMTF is
believed to be superior to the traditional solutions, which only focus on the
binary image-tag relations. During the model estimation, we employ a
ranking based optimization scheme to interpret the tagging data, in which
the pair-wise qualitative difference between positive and negative examples
is used, instead of the point-wise 0/1 confidence. Specifically, the positive
examples are directly decided by the observed user-image-tag interrelations,
while the negative ones are collected with respect to the most semantically
and contextually irrelevant tags. Extensive experiments on a benchmark
Flickr dataset demonstrate the effectiveness of the proposed solution for tag
refinement. We also show attractive performances on two potential
applications as the by-products of the ternary relation analysis.
3. Existing System:
Search by Image is optimized to work well for content that is reasonably
well described on the web. For this reason, you’ll likely get more relevant
results for famous landmarks or paintings than you will for more personal
images like your toddler’s latest finger painting.
Proposed System:
To explore solutions to overcome these two drawbacks, the noisy
or incomplete correspondence between the images and tags
prohibits them from being leveraged for precise image retrieval
and effective management. To tackle the problem of tag
refinement, we propose a method of Ranking based Multi-
correlation Tensor Factorization (RMTF), to jointly model the
ternary relations among user, image and tag, and further to
precisely reconstruct the user-aware image-tag associations as a
result.
4. Modules:
1. Login modules.
2. Visual Similarity module.
3. Semantic Correlation module.
4. RMTF Module.
Module Description:
1. Login modules.
Login or logon (also called logging in or on and signing in or on) is the
process by which individual access to a computer system is controlled by
identification of the user using credentials provided by the user.
A user can log in to a system and can then log out or log off (perform a
logout / logoff) when the access is no longer needed.
Logging out may be done explicitly by the user performing some action,
such as entering the appropriate command, or clicking a website link
labeled as such. It can also be done implicitly, such as by powering the
machine off, closing a web browser window, leaving a website, or not
refreshing a webpage within a defined period.
2. Visual Similarity module
In this module we are getting the images by Visual Similarity. We can
see the images by manually and we can identified what type of image is this
so that the way we can doing the social tagging with the image.
5. 3. Semantic Correlation module.
What we are interested in is the fact that the tags are annotated by
different users and there are variations in individual user’s perspective and
vocabulary. Incorporation of user may bring similar benefits to the image
understanding. On top of visual appearance, the fact that images from the
same user or tagged by similar users can capture more semantic correlations.
4. RMTF module.
In the RMTF module, we utilize tensor factorization to jointly model
the multiple factors. To make full use of the observed tagging data and
partial use of unobserved data, we present a novel ranking scheme for model
estimation, which is based on the pair-wise qualitative difference between
Positive examples (i.e., observed tagging data) and negative ones (i.e.,
partial unobserved data). The collection of negative examples is carried out
by analyzing user tagging behavior. The issue of noisy tags and missing tags
are considered in a conservative filtering strategy by exploiting the tag
correlation on context and semantics. Besides, the multiple intra-relations
are employed as the smoothness constraints and then the factors inference is
cast as a regularized tensor factorization problem. Finally, based on the
learnt factor representations, which encode the compact users, images and
tags representation over their latent subspaces, tag refinement is performed
by computing the cross-space image-tag associations.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 256 Mb.
6. Software Requirements:
• Operating system : - Windows XP Professional.
• Front End : - Visual Studio.Net 2008sp1
• Coding Language : - Asp .Net.
7. 3. Semantic Correlation module.
What we are interested in is the fact that the tags are annotated by
different users and there are variations in individual user’s perspective and
vocabulary. Incorporation of user may bring similar benefits to the image
understanding. On top of visual appearance, the fact that images from the
same user or tagged by similar users can capture more semantic correlations.
4. RMTF module.
In the RMTF module, we utilize tensor factorization to jointly model
the multiple factors. To make full use of the observed tagging data and
partial use of unobserved data, we present a novel ranking scheme for model
estimation, which is based on the pair-wise qualitative difference between
Positive examples (i.e., observed tagging data) and negative ones (i.e.,
partial unobserved data). The collection of negative examples is carried out
by analyzing user tagging behavior. The issue of noisy tags and missing tags
are considered in a conservative filtering strategy by exploiting the tag
correlation on context and semantics. Besides, the multiple intra-relations
are employed as the smoothness constraints and then the factors inference is
cast as a regularized tensor factorization problem. Finally, based on the
learnt factor representations, which encode the compact users, images and
tags representation over their latent subspaces, tag refinement is performed
by computing the cross-space image-tag associations.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 256 Mb.
