Abstract
Underwater Scene Search Turns Out to Be One of the Most Challenging Topics in the Underwater Image Analysis. in This Paper, We Present One Underwater Scene Search Scheme Combined with Similarity Measure and Sparse Representation. the Color Histogram is First Adopted to Classify the Candidate Image Patches for Each Kind of the Underwater Scene. at the Same Time, the Feature Similarity (FSIM) Considers a Full Reference of the Complementary Terms, I.e., the Phase Congruency (Pc) and the Image Gradient Magnitude (Gm), to Reflect and Generate the Similarity Map between the Query Image Patch and the Reference One. Sparse Representation is Then Made Full Use of to Represent the Underwater Image Patches. It is Shown in the Simulation Experiment that the Proposed Approach Could Achieve Great Performances in Both Robustness and Effectiveness with Good Behaviors in Underwater Scene Search Tasks.
Recommended Citation
Z. Wang et al., "Underwater Scene Search Scheme Via Similarity Measure and Sparse Representation for Autonomous Underwater Vehicle," OCEANS 2016 MTS/IEEE Monterey, OCE 2016, article no. 7761284, Institute of Electrical and Electronics Engineers, Nov 2016.
The definitive version is available at https://doi.org/10.1109/OCEANS.2016.7761284
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Color histogram; Gradient magnitude; Phase congruency; Sparse representation; Underwater scene search
International Standard Book Number (ISBN)
978-150901537-5
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
28 Nov 2016
