Online Visual Tracking ofWeighted Multiple Instance Learning via Neutrosophic Similarity-Based Objectness Estimation

Download or read book Online Visual Tracking ofWeighted Multiple Instance Learning via Neutrosophic Similarity-Based Objectness Estimation written by Keli Hu and published by Infinite Study. This book was released on with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: An online neutrosophic similarity-based objectness tracking with a weighted multiple instance learning algorithm (NeutWMIL) is proposed. Each training sample is extracted surrounding the object location, and the distribution of these samples is symmetric. To provide a more robust weight for each sample in the positive bag, the asymmetry of the importance of the samples is considered. The neutrosophic similarity-based objectness estimation with object properties (super straddling) is applied.