Content Based Image Processing Approach on Colored Images using KNN Classifier

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-36 Number-4
Year of Publication : 2016
Authors : Garvita, Priyanka Kamboj
  10.14445/22312803/IJCTT-V36P133

MLA

Garvita, Priyanka Kamboj "Content Based Image Processing Approach on Colored Images using KNN Classifier". International Journal of Computer Trends and Technology (IJCTT) V36(4):183-191 June 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Content Based Image Retrieval (CBIR) is also called as Query By Image Content (QBIC). It has been an active research field since last decades. In contrast with traditional systems, where images are retrieved on the basis of keywords but in CBIR system images are retrieved on the basis of visual content. Content Based Image Retrieval is a methodology that allows a user to extract an image based on a query from the database. Here, the term "content" refers to color, shape, texture, or any other data that can be derived from image itself. Different types of classification we can use Neural network, Support Vector Machine (SVM), KNN, Bayesian etc. In this paper, we are using K Nearest Neighbor (KNN) classifier to find out the relevant images and after that we use Spearman’s Rank Correlation Function to reduce the time complexity and improve F-measure.

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Keywords
KNN, F-measure, Recall, Precision, Spearman’s Rank Correlation Function, Test Dataset, Train Dataset, color strings.