Efficient foreground object segmentation from video by Probability Weighted Moments

Abstract Segmentation of foreground objects is an important issue in computer vision. Since, there exist no predefined classes for unsupervised learning, which makes it computationally expensive. Also, the contrast of foreground to background needs to be tackled effectively. To overcome the aforementioned problems, in this paper an efficient approach named Fused Probability weighted moments and Principal component analysis Video Object Segmentation (FPPVOS), is proposed by the fusion of Probability Weighted Moments (PWM) with Principal Component Analysis (PCA). FPPVOS significantly reduces the computational cost by taking into account the intra-class variability due to its less sensitivity towards the outliers and low sample variability. Theoretically, this fusion ensures the efficiency of the unsupervised learning where classification is assured, even though if no grouping or labels of the objects are available. The results are evaluated on the standard YouTube-Objects dataset. We achieved the preeminent performance by using FPPVOS while solving the problems of object separation and segmentation from its background in a reasonable time interval comparative to the state-of-the-art methods.