Recognition of Dog Breeds using Convolutional Neural Network and Visual Geometry Group
Anant Sharma1, Anwesh Sahoo2, Azhagiri M3, Diganta Dutta4

1Anant Sharma*, Computer Science, Srm Institute of Scince and Technology, Ramapuram campus, Chennai. India.
2Anwesh Sahoo, Computer Science, Srm Institute of Scince and Technology, Ramapuramcampus, Chennai. India.
3Azhagiri M, Computer Science, Srm Institute of Scince and Technology, Ramapuramcampus, Chennai. India.
4Diganta Dutta, Computer Science, Srm Institute of Scince and Technology, Ramapuramcampus, Chennai. India.
Manuscript received on September 19, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 3898-3902 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1263109119/2019©BEIESP | DOI: 10.35940/ijeat.A1263.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Due to excessive breeding or cross-breeding, the nature of an animal like a dog has varied a lot from years ago. Using Image processing for the breed analysis will predict the exact result/s with maximum accuracy, unlike naked eye recognition ADA boosting methodology is used for breed analysis and recognition. ADA Boosting creates a strong classifier from several weak classifiers. To separate the dog breeds from one another, we use Image processing classification. It predicts the predominant breed/s present in the canine with maximum accuracy. Since the dogs may be cross-breed or had cross-breed predecessors, they may have a variety of breeds present in them, so using Image processing Classification tools we find the correct breed/s. It will be essential for easy classification of the dogs based on breeds and it can provide proof that naked eye recognition of breeds is undependable or trivial. Using Image processing analysis, we can analyze and do recognition of various animals like sheep, cattle, etc.
Keywords: Ada boosting, Image processing, Classification, Deep learning, Cross-breeding.