A Supervised Stable Object Detection with Image Feature Extraction using Image Segmentation by Comparing Histogram of Oriented Gradients (HOG) Algorithm over Scale Invariant Feature Transform (SIFT) Algorithm Model. (Journal of Pharmaceutical Negative Results, 2022)
Aim: The objective of the research is to increase the accuracy of object detection using novel image segmentation using machine learning algorithms. Materials and Methods: The categorising is performed by adopting a sample size of n = 10 in Histogram of Oriented Gradients (HOG) and sample size n = 10 in Scale Invariant Feature Transform (SIFT) algorithms with a sample size = 10 and the G-Power analysis was carried out with 80% and confidence interval 95%. Results and Discussion: The analysis of the results shows that the Histogram Of Oriented Gradients (HOG) has a high accuracy of 92.49% in comparison with the Scale Invariant Feature Transform (SIFT) 86.30%. A statistically significant difference exists between the research groups with p=0.001 (2 tailed) (p<0.05). Conclusion: Detection of objects with high accuracy using machine learning algorithms shows that the regional proposal network based Faster R-CNN appears to generate better accuracy than the Selective search(Fast RCNN) algorithm.