Salient object detection based on deep clustering attention mechanism

Abstract

Objective: Salient object detection is a basic task in the field of computer vision, which simulates the human visual attention mechanism and quickly detects attractive objects in the scene that are most likely to represent user query variables and contain the most information. As a preprocessing step of other vision tasks, such as image resizing, visual tracking, person re-identification, and image segmentation, salient object detection plays a very important role. The traditional salient object detection method mainly uses the method of manually extracting features of the image to detect. However, this process is time-consuming and labor-intensive, and the results cannot meet the requirements. With the rise of deep learning, a large number of feature extraction algorithms based on convolutional neural networks have emerged. Compared with traditional feature extraction methods, using deep neural networks to extract features has better quality and more accurate prediction. In order to obtain accurate salient object segmentation results, deep learning-based methods mostly introduce attention mechanisms for feature weighting to suppress noise and redundant information. However, the modeling process of the existing attention mechanism is quite rough, which treats each position in the feature tensor equally and directly solves the attention score. This strategy cannot explicitly learn the global importance of different channels and different spatial regions, which may lead to missed detection or misdetection. To this end, in this study, we propose a deep clustering attention (DCA) mechanism to better model the feature-level pixel-by-pixel relationship. Method: In this study, the proposed DCA explicitly divides the feature tensors into several categories channel-wise and spatial-wise; that is, it clusters the features into foreground and background sensitive regions. Then, general per-pixel attention weighting is performed within each class, and semantical attention weighting is further performed inter-classes. The idea of DCA is easy to understand, whose parameter quantity is also small and can be deployed in any salient detection network. This method can efficiently separate the foreground and background regions. In addition, through supervised learning on the edges of salient objects, the prediction can get clearer edges, and the results are more accurate. Result: Comparison of 19 state-of-the-art methods on six large public datasets demonstrates the effectiveness of DCA in modeling pixel-wise attention, which is very helpful for obtaining finely salient object segmentation mask. On various evaluation indicators, the effects of the model after the deployment of DCA have improved. On the extended cornplex scene saliency(ECSSD) dataset, the performance of DCANet increased by 0.9% over the second place (F-measure value). On the Dalian University of Technology and OMRON Corporation(DUT-OMRON) dataset, the performance of DCANet increased by 0.5% over the second place (F-measure value), and the MAE decreased by 3.2%. On the HKU-IS dataset, the performance of DCANet is 0.3% higher than the second place (F-measure value), and the MAE is reduced by 2.8%. On the pattern analysis, statistical modeling and computational learning(PASCAL)-subset(S) dataset, the performance of DCANet is 0.8% higher than the second place (F-measure value), and the MAE is reduced by 4.2%. Conclusion: The DCA proposed in this study effectively enhances the globally salient scores of foreground sensitive classes through more fine-grained channel partitioning and spatial region partitioning. This paper analyzes the deficiencies of the existing salient object detection algorithm based on attention mechanism and proposes a method for explicitly dividing feature channels and spatial regions. The attention modeling mechanism helps the model training process perceive and adapt tasks quickly. Compared with the existing attention mechanism, the idea of DCA is clear, the effect is significant, and it is simple to deploy. Meanwhile, DCA provides a viable new research direction for the study of more general attention mechanisms.