sis 18(16): e13

Research Article

Using Co-occurrence and Granulometry Features for Content Based Image Retrieval

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  • @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
Lal Said 1,*, Khurram Khurshid 1, Asia Aman 1
  • 1: Department of Electrical Engineeering, Institute of Space Technology, Islamabad, 44000, Pakistan
*Contact email: engrlalsaid@yahoo.com

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.