Optimisation for image salient object detection based on semantic-aware clustering and CRF

• Published in: IET computer vision 2020-03, Vol.14 (2), p.49-58
• Main Authors: Chen, Junhao, Niu, Yuzhe, Wu, Jianbin, Chen, Junrong
• Format: Article
• Language: English
• Subjects: CRF; different imperfections; feature extraction; grid search method; image classification; image salient object detection; image semantic features; neural nets; object detection; object detection methods; optimisation; optimisation methods; pattern clustering; Research Article; saliency maps; salient object detection neglect; semantic-aware clustering; semantically similar images; training images
• ISSN: 1751-9632; 1751-9640; 1751-9640
• DOI: 10.1049/iet-cvi.2019.0063

State-of-the-art optimisation methods for salient object detection neglect that saliency maps of different images usually show different imperfections. Therefore, the saliency maps of some images cannot achieve effective optimisation. Based on the observation that the saliency maps of semantically similar images usually show similar imperfections, the authors propose an optimisation method for salient object detection based on semantic-aware clustering and conditional random field (CRF), named CCRF. They first cluster the training images into some clusters using the image semantic features extracted by using a deep convolutional neural network model for image classification. Then for each cluster, they use a CRF to optimise the saliency maps generated by existing salient object detection methods. A grid search method is used to compute the optimal weights of the kernels of the CRF. The saliency maps of the testing images are optimised by the corresponding CRFs with the optimal weights. The experimental results with 13 typical salient object detection methods on four datasets show that the proposed CCRF algorithm can effectively improve the results of a variety of image salient object detection methods and outperforms the compared optimisation methods.