Noise Tolerant Fine-Grained Visual Categorization with Fine Tuned Segmentation Via Deep Domain Adaption
Caroline El Fiorenza1, B. Sai Praneeth2, B. SaiSumanth3, M. Venkata Vijaya Rama Raju4, S. Teja Venkata Rama Raju5

1Caroline El Fiorenza, Assistant Professor, SRM University, Chennai (T.N), India.
2B. Sai Praneeth, UG Scholar, SRM University, Chennai (T.N), India.
3B. Sai Sumanth, UG Scholar, SRM University, Chennai (T.N), India.
4M. Venkata Vijaya Rama Raju, UG Scholar, SRM University, Chennai (T.N), India.
5S. Teja Venkata Rama Raju, UG Scholar, SRM University, Chennai (T.N), India.
Manuscript received on 03 June 2019 | Revised Manuscript received on 28 June 2019 | Manuscript Published on 04 July 2019 | PP: 355-358 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A10630681S419/2019©BEIESP
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Abstract: Image analysis techniques are playing a vital role in several applications.In general the applications involve the automatic extraction of features from the image which is further used for variety of classification purposes. In this paper we are going to propose an algorithm which will extract the image from the web and recognizes it and differentiates whether it is a living object or not and then classifies the image into different types using fine grained visual categorization and deep domain adaption. As a special topic in computer vision fine grained visual categorization has been attracting and getting attention these years, which is a advanced level problem for distinguishing between similar sub-ordinate classes.The algorithm will identify object and specifies whether it is a living or non-living object and then classification is done based on the object.
Keywords: Fine-Grained Visual Categorization, Data Scale, Artificial Neural Networks, Image Classification, Neural Networks, Medical Researches.
Scope of the Article: Visual Analytics