IoT 18: e1

Research Article

Shape Based Image Retrieval Using Fused Features

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  • @ARTICLE{10.4108/eai.31-10-2018.159916,
        author={Maria  Abro and Shahnawaz  Talpur and Nouman Qadeer  Soomro and Nazish  Aslam  Brohi},
        title={Shape Based Image Retrieval Using Fused Features},
        journal={EAI Endorsed Transactions on Internet of Things: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={IOT},
        year={2019},
        month={8},
        keywords={Fourier Descriptors, Hierarchical Centroids, Moment-based Features, SC Features, Features Fusion},
        doi={10.4108/eai.31-10-2018.159916}
    }
    
  • Maria Abro
    Shahnawaz Talpur
    Nouman Qadeer Soomro
    Nazish Aslam Brohi
    Year: 2019
    Shape Based Image Retrieval Using Fused Features
    IOT
    EAI
    DOI: 10.4108/eai.31-10-2018.159916
Maria Abro1,*, Shahnawaz Talpur1, Nouman Qadeer Soomro2, Nazish Aslam Brohi1
  • 1: Department of Computer System Engineering Mehran University of Engineering and Technology Jamshoro, Pakistan
  • 2: Mehran UET Kairpur Campus Sindh, Pakistan
*Contact email: abromaria102@gmail.com

Abstract

For content-based image retrieval, the shape is one of the most important discriminatory elements. The form captures most of the perceptual information of the observed objects on images in many applications, while colour and texture can often be omitted without affecting the performance of the retrieval. Unfortunately, there may be significant changes in shape, such as deformation, scaling, changes in orientation noise, and partial concealment. Accurate shape description remains, therefore, a challenging technical issue. The study performs experimental analysis to identify the problem. The adoption of the MPEG-7 and KIMIA-99 standard has significant importance to simplify the image retrieval process. The Fourier Descriptors, Moment-Based Features, Hierarchical Centroids and Histogram of Oriented Gradients have been applied for extraction of images from datasets. The fusion of features has been done by Discriminant Correlation Analysis and Direct Concatenation of features it has been evident that by fusion of features we obtained approximately 90% accurate and better results.