Maximizing a deep submodular function optimization with a weighted MAX-SAT problem for trajectory clustering and motion segmentation

Abstract

Computer vision models are commonly defined for maximum constrained submodular functions lies at the core of low-level and high-level models. In such that, the pixels that are to be grouped or segmenting moving object remains a challenging task. This paper proposes a joint framework for maximizing submodular energy subject to a matroid constraint using Deep Submodular Function (DSF) optimization approximately to solve the weighted MAX-SAT (Maximum Satisfiability) problem and a new trajectory clustering method called Simple Slice Linear clustering (SSLIC) and motion cue method for trajectory clustering and motion segmentation. In this objective function, the illustrative trajectories of a small number are selected automatically by deep submodular maximization. Although, the exploitation of monotone and submodular properties are further maximized and the complexity is reduced by a continuous greedy algorithm. The bound guarantees a fully sliced curve of (1- S/e) to (1–1/e) with less running time. Lastly, the motion is segmented by the motion cue method to accurately differentiate the set of frames for different scenes. Experiments on the Hopkins 155, Berkley Motion Segmentation (BMS) and FBMS-59 datasets display the trajectory clustering and motion segmentation result over its superior performance with respect to 14 quality evaluation metrics. Hence the simulation result shows that the proposed joint framework attains better performance than existing methods on trajectory clustering and motion segmentation task.