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Salient Object Detection Using DenseNet Features

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • 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)

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.

Suggested Citation

  • P. Kola Sujatha & N. Nivethan & R. Vignesh & G. Akila, 2020. "Salient Object Detection Using DenseNet Features," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1641-1648, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_168
    DOI: 10.1007/978-3-030-41862-5_168
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