Abstract
In This Paper, We Try to Combine Bag-Of-Words (Bow) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Together for One Kind of Sparse Representation in the Seafloor Visual Saliency Evaluation. Properties in the Water, Due to the Large Amount of Acoustic Noises, Sonar Signals Are Easily Polluted and Interfered during Image Collection, and the Sonar Images Usually Diverge from the True Underwater Environment or Degrade the Accuracy of the Measure, So Sparse Representation Has Been Taken into Consideration for Underwater Sonar Image Visual Saliency Evaluation. Our Method is a Simple and Computationally Efficient Extension of an Order less Bow and DBSCAN Model, and It Shows Consistently and Significantly Improved Performance on Sparse Representation in the Seafloor Visual Saliency Evaluation.
Recommended Citation
Y. Geng et al., "Seafloor Visual Saliency Evaluation for Navigation with Bow and Dbscan," OCEANS 2016 - Shanghai, article no. 7485697, Institute of Electrical and Electronics Engineers, Jun 2016.
The definitive version is available at https://doi.org/10.1109/OCEANSAP.2016.7485697
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Bag-of-Words; DBSCAN; SIFT; Sparse Representation; Visual Saliency Evaluation
International Standard Book Number (ISBN)
978-146739724-7
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
03 Jun 2016
