An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance Feedback

   International Journal of Managing Information Technology (IJMIT)

ISSN: 0975-5586 (Online); 0975-5926 (Print)

http://airccse.org/journal/ijmit/ijmit.html

Article:

An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance Feedback

Authors

Jayant Mishra1, Anubhav Sharma2 and Kapil Chaturvedi1, 1UIT, India and 2RITS, India

Abstract

Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solution.

Keywords

Content-based image retrieval, Color Histogram, Relevance Feedback, k-means. 

Paper URL

https://airccse.org/journal/ijmit/papers/3211ijmit02.pdf

Volume URL

https://airccse.org/journal/ijmit/vol3.html