Local feature descriptive modeling for natural images for image retrieval system

P.V.N. Reddy*, K. Satya Prasad**
* Professor & H.O.D of Electronics & Communications Engineering, Alfa College of Engineering &Technology, Allagadda, Andhra Pradesh.
** Professor of Electronics & Communications Engineering & Director of Evaluation, J.N.T.U Kakinada, Kakinada, Andhra Pradesh.
Periodicity:January - March'2010
DOI : https://doi.org/10.26634/jse.4.3.1114

Abstract

In this paper a retrieval study for natural image learning environment is proposed.  The content of this paper is a result of projects called Image content-based retrieval on a natural image database. The objective of this project is to develop an image content-based search engine, which can perform identity check of a natural image. It is well known that conventional natural image databases can only be retrieved by text-based query. In this paper we use the shape, color, and other features extracted from a captured natural image to search the natural image database. The developed technique is able to perform scale, translation, and rotation invariant matching between natural images. Currently, the database contains several hundreds of natural images. In future, we shall enhance the capability of the search engine to deal with more than 30,000 natural image species, which is the total amount of natural image species along the coast.

Keywords

Content-Based Image Retrieval CBIR, feature extraction, Information retrieval, Image retrieval, Image databases, Wireless Communication, Mobile Learning.

How to Cite this Article?

P.V.N. Reddy and K. Satya Prasad (2010). Local feature descriptive modeling for natural images for image retrieval system.i-manager’s Journal on Software Engineering, 4(3),23-27. https://doi.org/10.26634/jse.4.3.1114

References

[1]. Anami, B.S., Angadi, S.A, Amarapur, B., and Channal, S. (2000). “Invariant moment method for leaf image recognition”, Proceedings of the National conference on Document Analysis and Recognition, pp. 131-139.
[2]. Berman and Shapiro, L. (1999). “A flexible image data base system for content-based image retrieval”, Computer Vision and Image Understanding , 75(1/2):175–195.
[3]. Cai, W., Feng, D., & Fulton, R. (2000). “Content-Based Retrieval of Dynamic PET Functional Images,” IEEE Trans. On Information Technology in Biomedicine, Vol. 4, No. 2, pp. 152-158, June.
[4]. Carson et al., (1997). Region-based image querying. In Proc. of IEEE CVPR'97 Workshop on Content-Based Access of Image and Video Libraries, pp. 42-49, San Jan, Puerto Rico.
[5]. Chu, W.W., Hsu, C.C., and Taira, R.K. (1996). “A Knowledge-Based Approach for Retrieval Images by Content,” IEEE Transaction on knowledge and Data Engineering, Vol. 8, No. 4, pp. 522-532.
[6]. Furht, B., Met-Ling Shya, Shu-Ching Chen & Xiuqi Li. “An effective Content-based Visual Image retrival System”, In th Proceedings of 26 Annual International of Computer Software and Application Conference.
[7]. Gua-Dong Guo, Jain, A.K., Hong-Jiang Zhang & Wei- Ying Ma. (2001). “Learning similarity measure for natural image retrieval with relevance feed back”, In Proceeding of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognization.
[8]. Gudivada, V.N., and Raghavan, V.V. (1995). “Special issue on content-based image retrieval systems – guesteds”, IEEE Computer, 28(9).
[9]. Kankanhalli, M., Lee, W.F., & Mehtre, B.M. (1997). “Shape measures for content based image retrieval: A comparison,” Information Processing and Management, Vol. 33, No. 3.
[10]. Kyung-Ah Han and Sung-Hyun Myaemg, “Image Organization and Retrieval with Automatically Constructed Feature Vectors”.
[11]. Lu, G., and Sajjanhar, A. (1999). “Region-Based Shape Representation and Similarity Measure Suitable for Content-Based Image Retrieval”, Multimedia Systems.
[12]. Smeulders, Arnold, W.M., Theo givers ”Combining Colour and Shape Invariant features for image retrieval”.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.