IMAGE RETRIEVAL SYSTEM FOR NATURAL IMAGES BASED ON LOCAL FEATURES

Reddy P.V.N.1*, Satya Prasad K.2*
1Department of Electronics & Communications Engineering, Alfa College of Engineering &Technology, Allagadda, Kurnool, Andhra Pradesh, 518 543, India
2Department of Electronics & Communications Engineering, & Director of Evaluation, J.N.T.U Kakinada, Kakinada, Andhra Pradesh, 533 003, India
* Corresponding Author : kodati_prasad@rediffmail.com

Received : -     Accepted : -     Published : 21-12-2010
Volume : 2     Issue : 2       Pages : 1 - 4
Adv Inform Min 2.2 (2010):1-4

Keywords : Content-Based Image Retrieval CBIR, feature extraction, Information retrieval, Image retrieval, Image databases, Wireless Communication, Mobile Learning.
Conflict of Interest : None declared

Cite - MLA : Reddy P.V.N. and Satya Prasad K. "IMAGE RETRIEVAL SYSTEM FOR NATURAL IMAGES BASED ON LOCAL FEATURES." Advances in Information Mining 2.2 (2010):1-4.

Cite - APA : Reddy P.V.N., Satya Prasad K. (2010). IMAGE RETRIEVAL SYSTEM FOR NATURAL IMAGES BASED ON LOCAL FEATURES. Advances in Information Mining, 2 (2), 1-4.

Cite - Chicago : Reddy P.V.N. and Satya Prasad K. "IMAGE RETRIEVAL SYSTEM FOR NATURAL IMAGES BASED ON LOCAL FEATURES." Advances in Information Mining 2, no. 2 (2010):1-4.

Copyright : © 2010, Reddy P.V.N. and Satya Prasad K., Published by Bioinfo Publications. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

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.

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