Long-Term Cross-Session Relevance Feedback Using Virtual Features

ABSTRACT

Relevance feedback is an iterative process, which refines the retrievals by utilizing the user’s feedback on previously retrieved results. Traditional RF techniques solely use the short-term learning experience and do not exploit the knowledge created during cross sessions with multiple users. RF framework, which facilitates the combination of short term and long-term learning processes by integrating the traditional methods with a new technique called the virtual feature. The feedback history with all the users is digested by the system and is represented in a very efficient form as a virtual feature of the images. The dissimilarity measure can dynamically be adapted, depending on the estimate of the semantic relevance derived from the virtual features. In addition, with a dynamic database, the user’s subject concepts may transit from one to another. By monitoring the changes in retrieval performance, the proposed system can automatically adapt the concepts according to the new subject concepts. The experiments are conducted on a real image database. The results manifest that the proposed framework outperforms the traditional within-session and log-based long-term RF techniques.

EXISTING SYSTEM:

 The existing RF techniques deal with a single query in a single retrieval session only.

• There are no virtual features for session modification and maintenance
• They are using short term cross session
• Relevant information is collected online via the users’ feedback, and this information is very limited
• The system cannot output the retrieval results to a given query based on a sufficiently large set of training data
• The system has no knowledge about which database images are relevant and which are no relevant to a set of known labels, since we do not know the user’s intention until the user starts the feedback iteration.

PREOPOSED SYSTEM:

CBIR(Content Based Image retrieval) System modern image databases are queried by image content. Relevance feedback is an interactive process, which fulfills the requirements of the query formulation.

• The user initializes a query session by submitting an image.

• The system then compares the query image to each image in the database and returns the r images that are the nearest neighbors to the query.

• If the user is not satisfied with the retrieved result, the user can activate an RF process by identifying which retrieved images are relevant and which are non relevant.

• Based on the retrieved result users can give notification to the system which is relevant and which is non relevant this will store in virtual feature

• Virtual feature can adapt that reference with that image category for future effective retrievals

SYSTEM REQUIREMENTS

Hardware Requirements

            Processor                     :           Pentium III / IV

            Hard Disk                   :           40 GB

            Ram                             :           256 MB

Monitor                       :           15VGA Color

            Mouse                         :           Ball / Optical

            Keyboard                    :           102 Keys

           

Software Requirements

Operating System       :           Windows XP professional

Front End                    :           Microsoft Visual Studio .Net 2005

Language                    :           Visual C#.Net

Back End                    :           SQL Server 2000