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     Research Journal of Applied Sciences, Engineering and Technology


Content Based Image Retrieval Based Search Engines: A Clear Study and Comparison

Shriram K. Vasudevan, S.N. Abhishek, S. Swathi and Purnima Sri
Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham University, India
Research Journal of Applied Sciences, Engineering and Technology  2015  12:1378-1396
http://dx.doi.org/10.19026/rjaset.11.2245  |  © The Author(s) 2015
Received: June ‎17, ‎2015  |  Accepted: August ‎5, ‎2015  |  Published: December 25, 2015

Abstract

People use the technology to its fullest, but they are not aware of most of the technologies that are available to them and are still in search of those advancements that have already been made available to them. This study helps to understand the number of such availabilities and for what it can be used. Action speaks louder than words. Similarly an image speaks a thousand words than a thousand worded articles. With the rather fast moving world, we don’t find time to read an article, we would prefer to look at its diagrammatic representation and understand what is inside the article rather than reading it. To find any particular information about an author or creator or any document, we type in the keywords in the search bar and look for the required information. But what if you do not know any details about the author and all you do have is just an image. How will you figure out who is the person, what are his works, who has that particular picture of his and all others details you wish to know. This is where the Content Based Image Retrieval (CBIR) comes into picture. The availability of image search engines makes your work become much simpler. It’s not just for an image of humans, but those of animals, birds and dresses could also be crossed referenced using one among the several CBIR engines. With the development of technology people expect the results to be more accurate and approximate. Mere similarity between colours and texture of the material does not satisfy the user. The images that are compared and given as results seem to be the comparison of colour and category and the material, but not the actual comparison to that image. A proposed plan have been put forward which could bring about a change in the working of CBIR, giving more exact results. The plan states how to exactly compare the image rather than just the colour or texture.

Keywords:

Chic engine, immenselab, incogna , macroglossa , picalike , plan , TinEye , yandex,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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