Journal article Open Access

Hybrid Feature based Classification of Images using Supervised Methods for Tag Recommendation

Anupama D. Dondekar; Balwant A. Sonkamble


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        <foaf:name>Anupama D. Dondekar</foaf:name>
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            <foaf:name>Department of Computer Engineering, Pune Institute of Computer Technology, Pune, India.</foaf:name>
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        <foaf:name>Balwant A. Sonkamble</foaf:name>
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            <foaf:name>Department of Computer Engineering, Pune Institute of Computer Technology, Pune, India.</foaf:name>
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    <dct:title>Hybrid Feature based Classification of Images using Supervised Methods for Tag Recommendation</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued>
    <dcat:keyword>Image classification, Color Features, Texture Features, Supervised Classifier.</dcat:keyword>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-09-30</dct:issued>
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    <dct:description>&lt;p&gt;Recent advancement in digital technology and vast use of social image sharing websites leads to a huge database of images. On social websites the images are associated with the tags or keywords which describe the visual content of the images and other information. Theses tags are used by social image sharing websites for retrieval of the images. Therefore, it is important to assign appropriate tags to the images. To assign related tags, it is necessary to choose appropriate classifier for automatic classification of images into various sematic categories with respect to the classification accuracy which is important step for image tag recommendation. In this paper, three supervised classifier algorithms are implemented for image classifications which are SVM, kNN and random forest and its performance is analyzed on Flickr images. For classification of images, the features are extracted using color moment and wavelet packet descriptor.&lt;/p&gt;</dct:description>
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