ACTIVE LEARNING METHODS FOR INTERACTIVE IMAGE RETRIEVAL - IMAGE PROCESSING
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13-01-2010, 07:32 AM


ACTIVE LEARNING METHODS FOR INTERACTIVE IMAGE RETRIEVAL - IMAGE PROCESSING

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.
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04-10-2010, 03:55 PM


.doc   Active Learning Methods for Interactive Image Retrieval.doc (Size: 493.5 KB / Downloads: 131)
Active Learning Methods for Interactive Image Retrieval



Scope of the project and implimentation:

The aim is to build a fast and efficient strategy to retrieve the query
Concept in content-based image retrieval

Introduction:
Human interactive systems have attracted a lot of research interest in recent years, especially for content- based image retrieval systems. Contrary to the early systems, which focused on fully automatic strategies, recent approaches have introduced human-computer interaction. In this paper, we focus on the retrieval of concepts within a large image collection. We assume that a user is looking for a set of images, the query concept, within a database. The aim is to build a fast and efficient strategy to retrieve the query
Concept. In content-based image retrieval (CBIR), the search may be initiated using a query as an example. The top rank similar images are then presented to the user. Then, the interactive process allows the user to refine his request as much as necessary in
a relevance feedback loop. Many kinds of interaction between the user and the system have been proposed, but most of the time, user information consists of binary labels indicating whether or not the image belongs to the desired concept.
Module Description:


1) RGB Projections:

The RGB color model is an additive color model in which red, green, and blue light are added together in various ways to reproduce a broad array of colors. The name of the model comes from the initials of the three additive primary colors, red, green, and blue. The main purpose of the RGB color model is for the sensing, representation, and display of images in electronic systems, such as conventional photography.
In this module the RGB Projections is used to find the size of the image vertically and horizontally.
2) Image Utility:

Whenever minimizing the error of classification is interesting for CBIR, this criterion does not completely reflect the user satisfaction. Other utility criteria
Closer to this, such as precision, should provide more efficient selections.

3) Comparable Image:

In this module a reselection technique to speed up the selection process, which leads to a computational complexity negligible compared to the size of the database for the whole active learning process. All these components are integrated in our retrieval system, called RETIN and the user gives new labels for images, and they are compared to the current classification. If the user mostly gives relevant labels, the system should propose new images for labeling around a higher rank to get more irrelevant labels.

4) Similarity measure:

The results in terms of mean average precision according to the training set size (we omit the KFD which gives results very close to inductive SVMs) for both ANN and Corel databases. One can see that the classification-based methods give the best results, showing the power of statistical methods over geometrical approaches, like the one reported here (similarity refinement method).


5) Result:
Finally, the image will take the relevant image what the user search. One can see that we have selected concepts of different levels of complexities. The performances go from few percentages of Mean average precision to 89%. The concepts that are the most difficult to retrieve are very small and/or have a much diversified visual content. The method which aims at minimizing the error of generalization is the less efficient active learning method. The most efficient method is the precision- oriented method.

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13-10-2010, 05:49 PM



Active Learning Methods for Interactive Image Retrieval

Abstract:

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.












Existing System:

In the existing system the CBIR method faced a lot of disadvantage in case of the image retrival. The following are the main disadvantage faced in case of the medical field - Medical image description is an important problem in content-based medical image retrieval. Hierarchical medical image semantic features description model is proposed according to the main sources to get semantic features currently. Hence we propose the new algorithim to over come the existing system.

In existing system ,Images were first annotated with text and then searched using a text-based approach from traditional database management systems.


Proposed System:

 In case of the proposed system we use the following method to improve the efficiency. They are as follows.
 We implemented our models in a CBIR system for a specific application domain, the retrieval of coats of arms. We implemented altogether 19 features, including a color histogram, symmetry features.
• Content-based image retrieval, uses the visual contents of an image such as color, shape, texture, and spatial layout to represent and index the image














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
• KEYBOARD : 110 keys enhanced.

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


Reference:

[1] R. Veltkamp, “Content-based image retrieval system: A survey,” Tech. Rep., Univ. Utrecht, Utrecht, The Netherlands, 2002.

[2] Y. Rui, T. Huang, S. Mehrotra, and M. Ortega, “A relevance feedback architecture for content-based multimedia information retrieval systems,” in Proc. IEEE Workshop Content-Based Access of Image and Video Libraries, 1997, pp. 92–89.

[3] E. Chang, B. T. Li, G. Wu, and K Goh, “Statistical learning for effective visual information retrieval,” in Proc. IEEE Int. Conf. Image Processing, Barcelona, Spain, Sep. 2003, pp. 609–612.

[4] S. Aksoy, R. Haralick, F. Cheikh, and M. Gabbouj, “A weighted distance approach to relevance feedback,” in Proc. IAPR Int. Conf. Pattern Recognition, Barcelona, Spain, Sep. 3–8, 2000, vol. IV, pp. 812–815.

[5] J. Peng, B. Bhanu, and S. Qing, “Probabilistic feature relevance learning for content-based image retrieval,” Comput. Vis. Image Understand., vol. 75, no. 1-2, pp. 150–164, Jul.-Aug. 1999.









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vinayak_2612
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#4
23-12-2010, 05:18 PM

hey can anyone please upload the project and implimentation code for Active learning method for interactive image retrieval in MATLAB .modules required or the complete project and implimentation report.please do reply
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24-12-2010, 02:32 PM

hey i am trying to contact the person who did this project and implimentation ..
wait some time for upload the content by him
Use Search at http://topicideas.net/search.php wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion
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