sis 20(27): e7

Research Article

A Novel & Efficient Fusion Based Image Retrieval Model for Speedy Image Recovery

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  • @ARTICLE{10.4108/eai.9-3-2020.163832,
        author={Shefali Dhingra and Poonam Bansal},
        title={A Novel \& Efficient Fusion Based Image Retrieval Model for Speedy Image Recovery},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={7},
        number={27},
        publisher={EAI},
        journal_a={SIS},
        year={2020},
        month={3},
        keywords={Content based image retrieval, PCA, Clustering Based Indexing, Similarity-Based Indexing, Local binary pattern},
        doi={10.4108/eai.9-3-2020.163832}
    }
    
  • Shefali Dhingra
    Poonam Bansal
    Year: 2020
    A Novel & Efficient Fusion Based Image Retrieval Model for Speedy Image Recovery
    SIS
    EAI
    DOI: 10.4108/eai.9-3-2020.163832
Shefali Dhingra1,2,*, Poonam Bansal3
  • 1: Guru Gobind Singh Indraprastha University, New Delhi, India
  • 2: University Institute of Engineering & Technology, Kurukshetra University, Kurukshetra, India
  • 3: Maharaja Surajmal Institute of Technology, New Delhi, India
*Contact email: munjalshefali@gmail.com

Abstract

An efficient and novel image retrieval system is framed here, which retrieve images from massive datasets to overcome the constraints of efficiency and retrieval time. Thus to address this issue, an effective indexing technique is proposed on the hybrid system constituted by low level features of the image. Firstly, features are extracted from the combination color moment, LBP and segmentation to form a hybrid feature space. To reduce its dimensional space, principle component analysis is exercised which provide lesser and good quality features. On this space, two expedient indexing techniques are proposed: cluster based and similarity based. The approach that is proposed here is an innovative design of a hybrid content based image retrieval system, as in this framework all the skilled techniques are merged to form a competent and dynamic image retrieval system. Five touchstone datasets are used to test the performance of the system. Extensive experiments are carried out which shows that the system with cluster based indexing technique provides highlighted results as compared to similarity based technique and also surpasses the other latest state of art techniques in terms of precision and retrieval time.