关键词: MPLRR算法, 显著性目标, 凸包区域中心先验, 融合策略, 低秩模型

Abstract: In order to detect the salient object more accurately, a new salient object detection algorithm based on multiple prior fusion is proposed. Traditional center prior failed to detect salient object deviated from the center of image, the minimum convex hull is got by using the intersection of multi color space, and it can determine the location of the object and compute center prior by convex hull region. At the same time, a fusion strategy is proposed, which integrates the convex hull region center prior, color contrast prior and background prior into feature matrix. Finally, the saliency map is generated by the low rank matrix recovery model. The simulation experiments on the open dataset MSRA1000 and ESSCD show that MPLRR can obtain clear and significant salient object visual effect map. At the same time, F, AUC, MAE and other evaluation indicators are also significantly improved than many existing methods.

Key words: Multiple Prior fusion Low Rank Matrix Recovery（MPLRR） algorithm, salient object, convex hull region center prior, fusion strategy, low rank model