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SEMI SUPERVISED BIASED MAXIMUM MARGIN ANALYSIS FOR INTERACTIVE IMAGE RETRIEVAL

Platform : DOT NET

IEEE Projects Years : 2012 - 13

SEMI SUPERVISED BIASED MAXIMUM MARGIN ANALYSIS FOR INTERACTIVE IMAGE RETRIEVAL

ABSTRACT:

With many potential practical applications, content- based image retrieval (CBIR) has attracted substantial attention during the past few years. A variety of relevance feedback (RF) schemes have been developed as a powerful tool to bridge the semantic gap between low-level visual features and high-level semantic concepts, and thus to improve the performance of CBIR systems. Among various RF approaches, support-vector-machine

(SVM)-based RF is one of the most popular techniques in CBIR. Despite the success, directly using SVM as an RF scheme has two main drawbacks. First, it treats the positive and negative feedbacks equally, which is not appropriate since the two groups of training feedbacks have distinct properties. Second, most of the SVM-based RF techniques do not take into account the unlabeled samples, although they are very helpful in constructing a good classifier.

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. Color representations based content based image retrieval.

PROPOSED SYSTEM:

To explore solutions to overcome these two drawbacks, in this paper, we propose a biased maximum margin analysis (BMMA) and a semi supervised BMMA (Semi BMMA) for integrating the distinct properties of feedbacks and utilizing the information of unlabeled samples for SVM-based RF schemes. The BMMA differentiates positive feedbacks from negative ones based on local analysis, whereas the Semi BMMA can effectively integrate information of unlabeled samples by introducing a Laplacian regularize to the BMMA. We formally formulate this problem into a general subspace learning task and then propose an automatic approach of determining the dimensionality of the embedded subspace for RF. Extensive experiments on a large real-world image database demonstrate that the proposed scheme combined with the SVM RF can significantly improve the performance of CBIR systems.

 


HARDWARE & SOFTWARE REQUIREMENTS:

 

HARDWARE REQUIREMENTS:

 

  • System                                    :           Pentium IV 2.4 GHz.
  • Hard Disk                   :           40 GB.
  • Monitor                       :           15 VGA Colour.
  • Mouse                         :           Logitech.
  • Ram                             :           256 MB.

SOFTWARE REQUIREMENTS:

  • Operating system        : -         Windows XP Professional.
  • Front End                    : -         Visual Studio.Net 2005
  • Coding Language       : -         Visual C# .Net.

MODULES:

  1. 1.      LOGIN MODULES.
  2. 2.      POSITIVE MODULE.
  3. 3.      NEGATIVE MODULE.
  4. 4.      CBIR MODULE.

 

MODULE DESCRIPTION:

 

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.

POSITIVE MODULE:

With the observation that “all positive examples are alike; each negative example is negative in its own way,” the two groups of feedbacks have distinct properties for CBIR. However, the traditional SVM RF treats the positive and negative feedbacks equally. To alleviate the performance degradation when using SVM as an RF scheme for CBIR, we explore solutions based on the argument that different semantic concepts lie in different subspaces and each image can lie in many different concept subspaces .We formally formulate this problem into a general subspace learning problem and propose a BMMA for the SVM RF scheme..

NEGATIVE MODULE:

To utilize the information of unlabeled samples in the database, we introduced a Laplacian regularizer to the BMMA, which will lead to SemiBMMA for the SVM RF. The resultant Laplacian regularizer is largely based on the notion of local consistency, which was inspired by the recently emerging manifold learning community and can effectively depict the weak similarity relationship between unlabeled samples pairs. Then, the remaining images in the database are projected onto this resultant semantic subspace, and a similarity measure is applied to sort the images based on the new representations. For the SVM-based RFs, the distance to the hyperplane of the classifier is the criterion to discriminate the query-relevant samples from the query-irrelevant samples.

CBIR MODULE:

 

SVM-based RF has been widely used to bridge the semantic gap and enhance the performance of CBIR systems The novel approaches can distinguish the positive feedbacks and the negative feedbacks by maximizing the local margin and integrating the information of the unlabeled samples by introducing a Laplacian regularizer. Extensive experiments on a large real-world Corel image database have shown that the proposed scheme combined with the traditional SVM RF can significantly improve the performance of CBIR systems.

 




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