Page 25 - 《水产学报》2025年第11期

朱国平，等 水产学报, 2025, 49(11): 119102

life species identification challenges[C]//Proceedings of the 6th formes: Atherinidae) in eastern Algeria using somatic morpho-
International Conference of the Cross-Language Evaluation logy and otolith shape[J]. Italian Journal of Zoology, 2015,
Forum for European Languages. Toulouse: Springer, 2015: 462- 82(3): 446-453.
483. [35] Moura A, Muniz A A, Mullis E, et al. Population structure and
[24] Li D L, Wang Q, Li X, et al. Recent advances of machine vis- dynamics of the Atlantic mackerel (Scomber scombrus) in the
ion technology in fish classification[J]. ICES Journal of Marine North Atlantic inferred from otolith chemical and shape signa-
Science, 2022, 79(2): 263-284. tures[J]. Fisheries Research, 2020, 230: 105621.
[25] Sameer R A. A general overview on the categories of image [36] Lin Y J, Al-Abdulkader K. Identification of fish families and
features extraction techniques: a survey[J]. Journal of Al- species from the western Arabian Gulf by otolith shape ana-
Qadisiyah for Computer Science and Mathematics, 2023, 15(1): lysis and factors affecting the identification process[J]. Marine
179-188. and Freshwater Research, 2019, 70(12): 1818-1827.
[26] Kartika D S Y, Herumurti D. Koi fish classification based on [37] Dhawal R S, Chen L. A copula based method for fish species
HSV color space[C]//Proceedings of 2016 International Confer- classification[C]//Proceedings of the IEEE 15th International
ence on Information & Communication Technology and Sys- Conference on Cognitive Informatics & Cognitive Computing.
tems (ICTS). Surabaya, Indonesia: IEEE, 2016: 96-100. Palo Alto: IEEE, 2016: 471-478.
[27] Joo D, Kwan Y S, Song J, et al. Identification of cichlid fishes [38] Prasetyo E, Suciati N, Fatichah C. Multi-level residual network
from Lake Malawi using computer vision[J]. PLoS One, 2013, VGGNet for fish species classification[J]. Journal of King Saud
8(10): e77686. University-Computer and Information Sciences, 2022, 34(8):
[28] Hu J, Li D L, Duan Q L, et al. Fish species classification by 5286-5295.
color, texture and multi-class support vector machine using [39] Lin H Y, Tseng S L, Li J Y. SUR-Net: a deep network for fish
computer vision[J]. Computers and Electronics in Agriculture, detection and segmentation with limited training data[J]. IEEE
2012, 88: 133-140. Sensors Journal, 2022, 22(18): 18035-18044.
[29] Palazzo S, Kavasidis I, Spampinato C. Covariance based mod- [40] Weiss K, Khoshgoftaar T M, Wang D D. A survey of transfer
eling of underwater scenes for fish detection[C]//Proceedings of learning[J]. Journal of Big Data, 2016, 3(1): 9.
2013 IEEE International Conference on Image Processing. Mel- [41] Allken V, Handegard N O, Rosen S, et al. Fish species identi-
bourne: IEEE, 2013: 1481-1485. fication using a convolutional neural network trained on syn-
[30] Tharwat A, Hemedan A A, Hassanien A E, et al. A biometric- thetic data[J]. ICES Journal of Marine Science, 2019, 76(1):
based model for fish species classification[J]. Fisheries 342-349.
Research, 2018, 204: 324-336. [42] Weiss K, Khoshgoftaar T M, Wang D D. A survey of transfer
[31] Fouad M M M, Zawbaa H M, El-Bendary N, et al. Automatic learning[J]. Journal of Big data, 2016, 3(1): 1-40.
Nile tilapia fish classification approach using machine learning [43] Johnson D S, Véliz D, Scott S. Morphological differentiation in
techniques[C]//Proceedings of the 13th International Confer- the genus Cheirodon (Ostariophysi: Characidae) using both tra-
ence on Hybrid Intelligent Systems. Gammarth: IEEE, 2013: ditional and geometric morphometrics[J]. Gayana, 2012, 76(2):
173-178. 142-152.
[32] Rodrigues M T A, Freitas M H G, Pádua F L C, et al. Evaluat- [44] Güney S, Atasoy A. Discrimination of different fish species by
ing cluster detection algorithms and feature extraction tech- E-nose[C]//Proceedings of the 38th International Conference on
niques in automatic classification of fish species[J]. Pattern Telecommunications and Signal Processing. Prague: IEEE,
Analysis and Applications, 2015, 18(4): 783-797. 2015: 1-5.
[33] Lin C S, Hwang C L. New forms of shape invariants from [45] Ponton D. Is geometric morphometrics efficient for comparing
elliptic Fourier descriptors[J]. Pattern recognition, 1987, 20(5): otolith shape of different fish species?[J]. Journal of Morpho-
535-545. logy, 2006, 267(6): 750-757.
[34] Boudinar A S, Chaoui L, Mahe K, et al. Habitat discrimination [46] Chen Y W, Zhu G P. Using machine learning to alleviate the
of big-scale sand smelt Atherina boyeri Risso, 1810 (Atherini- allometric effect in otolith shape-based species discrimination:

中国水产学会主办 sponsored by China Society of Fisheries https://www.china-fishery.cn
11