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.
http://hal.archives-ouvertes.fr/docs/00/...in08ip.pdf
