Blind proposal quality assessment via deep objectness representation and local linear regression

Abstract

The quality of object proposal plays an important role in boosting the performance of many computer vision tasks, such as, object detection and recognition. Due to the absence of manually annotated bounding-box in practice, the quality metric towards blind assessment of object proposal is highly desirable for singling out the optimal proposals. In this paper, we propose a blind proposal quality assessment algorithm based on the Deep Objectness Representation and Local Linear Regression (DORLLR). Inspired by the hierarchy model of the human vision system, a deep convolutional neural network is developed to extract the objectness-aware image feature. Then, the local linear regression method is utilized to map the image feature to a quality score, which tries to evaluate each individual test window based on its k-nearest-neighbors. Experimental results on a large-scale IoU labeled dataset verify that the proposed method significantly outperforms the state-of-the-art blind proposal evaluation metrics.