Research Article
Using Co-occurrence and Granulometry Features for Content Based Image Retrieval
@ARTICLE{10.4108/eai.13-4-2018.154479, author={Lal Said and Khurram Khurshid and Asia Aman }, title={Using Co-occurrence and Granulometry Features for Content Based Image Retrieval}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={5}, number={16}, publisher={EAI}, journal_a={SIS}, year={2018}, month={4}, keywords={Granulometry Features, CCF, Content Based Image Retrieval}, doi={10.4108/eai.13-4-2018.154479} }- Lal Said
Khurram Khurshid
Asia Aman
Year: 2018
Using Co-occurrence and Granulometry Features for Content Based Image Retrieval
SIS
EAI
DOI: 10.4108/eai.13-4-2018.154479
Abstract
This communication presents a novel system for Content Based Image Retrieval (CBIR) using Granulometry and Color Co-occurrence Features (CCF). These features are extracted directly from images using visual codebook. Relative distance measures are used to identify the similarity between the stored images and the query image. Results show that proposed method of using Granulometry and CCF is superior to most state of the art CBIR systems. The proposed system is tested on Wang image database that contains 1000 images having different categories. The performance of the system, quantified using the Average Precision Rate (APR), is very encouraging.
Copyright © 2018 Lal Said et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
