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A Review on Semi-Supervised SVM Technique for Mining and Refining Weak Labels of Web Facial Images


Author : Patel Safiya and Pramod Bhalerao

Pages : 1771-1773
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Abstract

Today’s world is the world of smart phones, internet, multimedia which has made the art of capturing images or image data very immense as well as easy. This has flooded the web with the ample of images all over & the problem of annotating the images and labeling them arises too. Sometimes, it may happen that we may search the web for a person’s image but we may get some different or irrelevant image because the labels of these images may be weak, noisy or with incomplete names. Therefore, this paper proposes to identify weak labeled images using the SBFA technique and refining these labels automatically through semi-supervised SVM (Support Vector Machines) method as because semi-supervised learning requires less human effort and gives higher accuracy and hence we chose this technique because it is expected to give better results when reviewed analysis with other unsupervised and supervised learning techniques. Moreover in order to tackle the problems of optimization and scalability we can use different algorithms to overcome them.

Keywords: Annotation, SBFA, machine learning, SVM, S3VM, CBIR

Article published in International Journal of Current Engineering and Technology, Vol.5, No.3 (June-2015)

 

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