Abstract
The goal of content based image retrieval is to retrieve the images that users want to search. Content based Image retrieval systems attempt to search through a database to find images that are perceptually similar to a query image. Set of low-Level visual features (Color, Shape and Texture) are used to represent an image in most modern content based image retrieval systems. Therefore, between high-level information and low-level features a gap exists, which are the main reason that down the improvement of the image retrieval accuracy.In this paper Hybrid support vector machine (SVM) method proposed to retrieve several features and shorten the semantic gap between low-level visual feature and high-level perception. Image data set is taken from coral image data set. MATLAB 2009a is used as a simulator to analysis the proposed work.
Highlights
From the last few years, in the field of searching multimedia content on browsers Content-Based Image Retrieval (CBIR) has gained more attention [1] [2]
Content based Image retrieval systems attempt to search through a database to find images that are perceptually similar to a query image
An image with more than one example images used as an input in a CBIR system, CBIR system searches the result by using colour, texture and shape of the input image these features of an image is known as a low level features
Summary
From the last few years, in the field of searching multimedia content on browsers Content-Based Image Retrieval (CBIR) has gained more attention [1] [2]. An image with more than one example images used as an input in a CBIR system, CBIR system searches the result by using colour, texture and shape of the input image these features of an image is known as a low level features. For effective searching it is important to classify image features. It is important that low level features extracted from an input image may define the highlevel semantic concepts. Where a query image is taken as input CBIR system extract its features based on colour, texture and shape. Several methods are used to narrow the semantic gap between low level features andhigh level features. Meenakshi Pandey et al, International Journal of Advanced Research in Computer Science, 9 (2), March-April 2018, 638-645
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Research in Computer Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
