Back to Search
Start Over
Depth scale balance saliency detection with connective feature pyramid and edge guidance
- Source :
- Applied Intelligence. 51:5775-5792
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Convolutional Neural Networks (CNNs) have played an important role in saliency detection. How to detect a salient object as a whole is a key issue. However, most existing learning-based methods are not accurate enough to detect salient objects in complex scenes, such as easily overlooked small salient areas in a whole salient object, which is called scale imbalance problem in this paper. To address this issue, Scale Balance Network (SBN) based on fully convolutional network is proposed to accurately recognize and comprehensively detect salient objects. Firstly, to detect more small salient areas, a specially designed backbone instead of common backbone is adopted in this paper, which can capture larger resolution with more spatial features in deeper layers. Secondly, we present a novel progressive pyramid mechanism named Connective Feature Pyramid Module (CFPM), aiming to make the network focus on the balance between the large salient areas and the small ones. Finally, we present an Edge Enhancement Architecture with Various Kernels (EEAVK) to locate the saliency maps and refine the boundary features. Experimental results on five benchmark datasets show that the proposed SBN method achieves consistently superior performance in comparison with other state-of-the-art ones under different evaluation metrics.
- Subjects :
- Computer science
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Pattern recognition
02 engineering and technology
Edge enhancement
Convolutional neural network
Artificial Intelligence
Salience (neuroscience)
Feature (computer vision)
Salient
Pyramid
0202 electrical engineering, electronic engineering, information engineering
Benchmark (computing)
020201 artificial intelligence & image processing
Enhanced Data Rates for GSM Evolution
Pyramid (image processing)
Artificial intelligence
Focus (optics)
business
Subjects
Details
- ISSN :
- 15737497 and 0924669X
- Volume :
- 51
- Database :
- OpenAIRE
- Journal :
- Applied Intelligence
- Accession number :
- edsair.doi...........0db615d8a332bddbb897a73eba4ed163