CBIR using SIFT& FDCT with Relevance Feedback Mechanism
K Sugamya1, Suresh Pabboju2, A Vinaya Babu3

1K Sugamya, Chaitanya Bharathi Institute of Technology, Hyderabad, India. 
2Suresh Pabboju, Prof & Head ,Dept. of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, India. 
3A Vinaya Babu, Prof. & Dean, Stanley College of Engineering and Technology, Hyderabad, India
Manuscript received on 27 August 2019. | Revised Manuscript received on 11 September 2019. | Manuscript published on 30 September 2019. | PP: 1103-1108 | Volume-8 Issue-11, September 2019. | Retrieval Number: J11930881019/2019©BEIESP | DOI: 10.35940/ijitee.J1193.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Content-based image retrieval is a technique which uses visual contents to search images from large scale image databases according to users’ interests, has been an active and fast advancing research area since the 1990s. During the past decade, remarkable progress has been made in both theoretical research and system development. However, there remain many challenging research problems that continue to attract researchers from multiple disciplines. Some of the techniques used in CBIR are Query by example, Semantic retrieval, content comparison techniques etc. Most of the existing works were done on spatial domain which is not so efficient. To overcome the difficulties of the existing works, a new algorithm is planned. And the proposed approach is based on the frequency domain for the content based Image retrieval systems.A new image retrieval technique which will retrieve images from image databases based on their contents in frequency domain to get better results. And a relevance feedback method is used for improving the retrieval efficiency. Many techniques are there in this computer vision and image processing field. But using low level features as a basis and retrieve features with good efficiency is problem of the study. The proposed work relates Feature extraction using both frequency domain as well as spatial domain. For spatial domain SIFT and for frequency domain FDCT techniques are applied and results were compared to find better information retrieval.
Keywords: FDCT (Fast discrete curvelet Transform), SIFT (scale invariant function transform), SVM (support vector machine )
Scope of the Article: Aggregation, Integration, and Transformation