Performance Analysis of SIFT/SURF Algorithms in Neural Networks for Optimized Feature Detection
Aditya Retissin1, Mohammed Jasim2
1Aditya Retissin, Department of Software Engineering, SRM Institute of Science and Technology , Chennai (Tamil Nadu), India.
2Mohammed Jasim J S, Department of Mechatronics Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 15 July 2019 | Revised Manuscript received on 24 July 2019 | Manuscript Published on 01 August 2019 | PP: 51-56 | Volume-8 Issue-4S2, April 2019 | Retrieval Number: D10040484S219/19©BEIESP | DOI: 10.35940/ijeat.D1004.0484S219
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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: This paper is an experiment on the implementation of scale-invariant feature transform (SIFT) and speeded up robust features (SURF) algorithms into multi-dimensional neural networks. We are attempting to perform a comparative performance evaluation by using different scale factors of the SIFT algorithm in multi-layered neural networks. This method will help us to understand the best way of implementing the above algorithms in neural networks and from a given sample, extracting distinctive invariant features and finding points of interests. Hence performing a large data set computation would be made much easier because of the neural network implementation. The conventional method of performing SIFT has computational limitations and we aim to achieve best possible way of performing the feature detection when using SIFT and neural network combined, hence transcending computational limitations that SIFT previously had. This approach to recognition of features can robustly find results much faster on bigger dataset and at the same time have the benefits of SIFT algorithm.
Keywords: Machine Learning, Computer Vision, Neural Network, SIFT (Scale Invariant Feature Transform).
Scope of the Article: Machine Learning