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

Abstract

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. The neutrosophic theory is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. By considering the surrounding information of the object, a single valued neutrosophic set (SVNS)-based segmentation parameter selection method is proposed, to produce a well-built set of superpixels which can better explain the object area at each frame. Then, the intersection and shape-distance criteria are proposed for weighting each superpixel in the SVNS domain, mainly via three membership functions, T (truth), I (indeterminacy), and F (falsity), for each criterion. After filtering out the superpixels with low response, the newly defined neutrosophic weights are utilized for weighting each sample. Furthermore, the objectness estimation information is also applied for estimating and alleviating the problem of tracking drift. Experimental results on challenging benchmark video sequences reveal the superior performance of our algorithm when confronting appearance changes and background clutters.

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

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Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

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Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

“…As a result, more and more researchers have started introducing swarm intelligence algorithm (SIOA) into the traditional MTIS to improve the segmentation efficiency instead of the traditional exhaustive method. These SIOAs has offered greater efficiency in optimization tasks such as expensive optimization problems [ 32 , 33 ], medical diagnosis [ [34] , [35] , [36] , [37] ], PID optimization control [ [38] , [39] , [40] ], plant disease recognition [ 41 ], feature selection [ [42] , [43] , [44] , [45] ], object tracking [ 46 , 47 ], economic emission dispatch problem [ 48 ], engineering design [ [49] , [50] , [51] ], parameter tuning for machine learning models [ [52] , [53] , [54] ], constrained optimization problems [ 55 , 56 ], combination optimization problems [ 57 ], traveling salesman problem [ 58 ], multi-objective or many optimization problems [ [59] , [60] , [61] ], and scheduling problems [ [62] , [63] , [64] ].…”