Salient Object Detection Using DenseNet Features

P. Kola Sujatha, N. Nivethan, R. Vignesh and G. Akila
Additional contact information
P. Kola Sujatha: Madras Institute of Technology, Department of Information Technology
N. Nivethan: Madras Institute of Technology, Department of Information Technology
R. Vignesh: Madras Institute of Technology, Department of Information Technology
G. Akila: Madras Institute of Technology, Department of Information Technology

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1641-1648 from Springer

Abstract: Abstract Salient object detection in an image attributes to finding an object which stands out from its neighbors. State of the art approaches in salient object detection have used learning-based methods for predicting saliency maps. Typically the features from the images are extracted using CNN architectures as they have become influential in computer vision tasks. In this paper, a bottom-up approach for salient object detection in images is described. Densely Connected Neural Network (DenseNet), a recent CNN architecture which has shown significant improvement in classification tasks, has been used for extracting features from the image. DenseNet has strengthened feature propagation, reduced training parameters and also has a lower error rate compared to other CNN architectures. Features from DenseNet have been used to predict the saliency maps of the images. The experimental results show significant improvements from previous works on saliency.

Keywords: Saliency detection; DenseNet; Bottom-up approach; Feature extraction (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_168

Ordering information: This item can be ordered from
http://www.springer.com/9783030418625

DOI: 10.1007/978-3-030-41862-5_168

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().