Strength for Today and Bright Hope for Tomorrow

Volume 1:2 March 2016

Chief Editor
Dr. D. Nagarathinam, M.E., Ph.D.

         Dr. P. N. Rajnarayanan, M.E., Ph.D.
         Dr. K. Sudalaimani, M.E., Ph.D.
         Dr. S. Ramanathan, Ph.D. (Chemistry)

Language and Style Advisors
         G. Baskaran, Ph.D.
         Sam Mohanlal, Ph.D.

Executive Editor
         M. S. Thirumalai, Ph.D.

Back Issues of Engineering & Technology in India - From February 2016




  • E-mail your articles and book-length reports to
  • Your articles and book-length reports should be written following standard Stylesheets such as ASME reference and citation models.
  • The Editorial Board has the right to accept, reject, or suggest modifications to the articles submitted for publication, and to make suitable stylistic adjustments. High quality, academic integrity, ethics and morals are expected from the authors and discussants.
  • Would you like to announce the dates and venues of your conferences, seminars, etc., and also publish the outline proceedings of these programs? Send a report to Engineering & Technology in India,

Copyright © 2015
M. S. Thirumalai

Features Extraction in Context Based Image Retrieval

Aravindh A.S. and J. Christy Samuel


There has been a growing interest in exploiting contextual information in addition to local features to detect and localize multiple object categories in an image. A context model can rule out some unlikely combinations or locations of objects and guide detectors to produce a semantically coherent interpretation of a scene. Our model incorporates global image features, dependencies between object categories, and outputs of local detectors into one probabilistic framework. We demonstrate that our context model improves object recognition performance and provides a coherent interpretation of a scene, which enables a reliable image querying system by multiple object categories. In addition, our model can be applied to scene understanding tasks that local detectors alone cannot solve, such as detecting objects out of context or querying for the most typical and the least typical scenes in a data set.

Keywords: image retrieval, context-based extraction, querying system, localization.

1.1 Edge Detection

Edge detection is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in 1D signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction.

This is only the beginning part of the project report. PLEASE CLICK HERE TO READ THE ENTIRE REPORT IN PRINTER-FRIENDLY VERSION.

Aravindh A.S., B.Tech.

J. Christy Samuel