Name
Title | ||
|---|---|---|
Image Saliency Prediction in Transformed Domain: A Deep Complex Neural Network Method |
Abstract | ||
|---|---|---|
The transformed domain fearures of images show effectiveness in distinguishing salient and non-salient regions. In this paper, we propose a novel deep complex neural network, named Sal-DCNN, to predict image saliency by learning features in both pixel and transformed domains. Before proposing Sal-DCNN, we analyze the saliency cues encoded in discrete Fourier transform (DFT) domain. Consequently, we have the following findings: 1) the phase spectrum encodes most saliency cues; 2) a certain pattern of the amplitude spectrum is important for saliency prediction; 3) the transformed domain spectrum is robust to noise and down-sampling for saliency prediction. According to these findings, we develop the structure of Sal-DCNN, including two main stages: the complex dense encoder and three-stream multi-domain decoder. Given the new Sal-DCNN structure, the saliency maps can be predicted under the supervision of ground-truth fixation maps in both pixel and transformed domains. Finally, the experimental results show that our Sal-DCNN method outperforms other 8 state-of-the-art methods for image saliency prediction on 3 databases. |
Year | Venue | Field |
|---|---|---|
2019 | THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Pattern recognition,Computer science,Salience (neuroscience),Artificial intelligence,Artificial neural network,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lai Jiang | 1 | 37 | 7.31 |
| Zhe Wang | 2 | 0 | 1.01 |
| Mai Xu | 3 | 509 | 57.90 |
| Zulin Wang | 4 | 68 | 8.27 |