Salient Object Detection With Importance Degree

In this article, we introduce salient object detection with importance degree (SOD-ID), which is a generalized technique for salient object detection (SOD), and propose an SOD-ID method. We define SOD-ID as a technique that detects salient objects and estimates their importance degree values. Hence, it is more effective for some image applications than SOD, which is shown via examples. The definition, evaluation procedure, and data collection for SOD-ID are introduced and discussed, and we propose its evaluation metric and data preparation, whose validity is discussed with the simulation results. Moreover, we propose an SOD-ID method, which consists of three technical blocks: instance segmentation, saliency detection, and importance degree estimation. The saliency detection block is proposed based on a convolutional neural network using the results of the instance segmentation block. The importance degree estimation block is achieved using the results of the other blocks. The proposed method accurately suppresses inaccurate saliencies and estimates the importance degree for multi-object images. In the simulations, the proposed method outperformed state-of-the-art methods with respect to the F-measure for SOD; and Spearman's and Kendall rank correlation coefficients, and the proposed metric for SOD-ID.