Over-atoms accumulation orthogonal matching pursuit reconstruction algorithm for fish recognition and identification

摘要

Fish recognition and identification in an underwater environment are important research topics. In this study, several real-world underwater videos were collected to construct a fish category database for further fish recognition and identification. Recently, compressive sensing, using reconstruction algorithms to reconstruct a sparse signal, has been successfully applied to face recognition. Reconstruction algorithms can be roughly categorized into two groups: basic pursuit (BP) and matching pursuit (MP). BP-related methods adopt a convex optimization technique, while MP-related methods utilize greedy search and vector projection ideas. This study reviews concepts for these reconstruction algorithms and analyzes their performance. Moreover, an over-atoms accumulation orthogonal matching pursuit (OAOMP) method based on OMP is proposed. OAOMP includes two procedures: picking over atoms, and accumulating weighting coefficients of each subject to assign as new weights. OAOMP was compared with existing reconstruction algorithms in terms of reconstruction performance and run time. Experiments were implemented in a fish category database by using eigenfaces and fisherfaces for feature extraction. The experimental results demonstrated that BP-related methods have better recognition rates, while MP-related methods have shorter run times. Moreover, OAOMP is able to achieve better accuracy than OMP and other MP-related methods.