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Content and context based image retrieval classification based on firefly-neural network

  • Thiriveedhi Yellamanda Srinivasa Rao
  • Pakanati Chenna Reddy
Article
  • 2 Downloads

Abstract

In this study presents an improved classification and feature reduction techniques are newly introduced in our proposed search area of image retrieval. Especially we are including a shape feature that has been so active and successful in the past few years. In our proposed approach the texture feature is extracted by using improved multi texton technique and GLCM technique and the textual features are keywords, annotations. The Visual features are extracted with the aid of a bag of visual words (BOW) also it can be approved by employ Scaling Invariant Feature Transform (SIFT) and Fuzzy C-Means Clustering technique the features are edge detection, corner detection. Improving a performance and accuracy newly we are including a shape based features the features are Active Appearance Model (AAM) and shape surface plot (SURF). In the second phase, we are including feature reduction method is applied to reduce the features’ space without losing the accuracy of classification in this reduction phases we are including an OLPP technique to perform a feature reduction phases. After that, the images are classified with the aid of modified neural network algorithm. This will classify the images in the different class in order to improve the precision and recall rate. After classification, we are including a squared Euclidian distance method to retrieve minimum distance image the similar images are retrieved from the relevant class as per the given query image.

Keywords

Scaling invariant feature transform Fuzzy C-means clustering Active appearance model Shape surface plot Modified neural network Squared Euclidian distance 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Thiriveedhi Yellamanda Srinivasa Rao
    • 1
  • Pakanati Chenna Reddy
    • 2
  1. 1.Department of Computer Science and EngineeringJNTUKKakinadaIndia
  2. 2.Department of Computer Science and EngineeringJNTUAnantapurIndia

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