Object detection from Background Scene Using t-SNE-ORB Gradient Boost

Radhamadhab Dalai, Kumar Kishore, Senapati
unpublished
Object Classification using Gradient Boost is a robust mechanism used for computer vision problem domain. The acceleration of t-SNE-an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots-using two tree-based algorithms. In particular, the paper develops variants of the Barnes-Hut algorithm and of the dual-tree algorithm that approximate the gradient used for learning t-SNE embeddings in O (NlogN).Complex background adds challenge and error
more » ... rgin as well to the problem significantly lot algorithms for object detection are hard to comply with occlusion and pixel bending moment affect. In this paper a highly robust algorithm for gradient boost based t-SNE[16] for a less resolution image has been proposed and implemented using ORB detection with gradient boosting machine learning algortihm.The work has been compared with Adaboost and Surf based technology. The analysis of result shows 4.2% increase in performance of earlier model. The feature points extracted from ORB method are further processed to reduce the processing further. Only those points are selected which are triangularly farthest from centroid of it and only 1 point of feature selected. Thus the result is around 28%, much faster than earlier computation. The tree based GB has been implemented in this algorithm. With more number of feature points more classes need to be recognized and hence the computations performed is required an unreasonable amount of effort and time. So some nearby classes are assigned at same level using our algorithm to reduce the number of tree nodes. Overall performance of the proposed algorithm shows a significant increase in efficiency in computation time.
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