Fish Recognition Ground-Truth data


This fish data is acquired from a live video dataset resulting in 27370 verified fish images. The whole dataset is divided into 23 clusters and each cluster is presented by a representative species, which is based on the synapomorphies characteristic from the extent that the taxon is monophyletic. The representative image indicates the distinction between clusters shown in the figure below, e.g. the presence or absence of components (anal-fin, nasal, infraorbitals), specific number (six dorsal-fin spines, two spiny dorsal-fins), particular shape (second dorsal-fin spine long), etc. This figure shows the representative fish species name and the numbers of detections. The data is very imbalanced where the most frequent species is about 1000 times more than the least one. The fish detection and tracking software described in [1] is used to obtain the fish images. The fish species are manually labeled by following instructions from marine biologists [2].


This data is organized into 23 groups, where the fish images and their masks are stored separately. Each cluster has a single package. The image files are named as "tracking id_fish id". Fish images with the same "tracking id" means they are belong to the same trajectory. "fish id" is a global unique id, which ranges from 1 to 27370. A reverse table contains "file name verse cluster id" is provided at here. The whole package of all groups is available here (510,912,000 bytes, checkSum).

 ID.species  Detection #  Trajectory #  Fish image  Mask image
 01.Dascyllus reticulatus   12112   4240   fish_01.tar    checkSum  mask_01.tar    checkSum
 02.Plectroglyphidodon dickii   2683  1225  fish_02.tar    checkSum  mask_02.tar    checkSum
 03.Chromis chrysura   3593  1175  fish_03.tar    checkSum  mask_03.tar    checkSum
 04.Amphiprion clarkii   4049  1021  fish_04.tar    checkSum  mask_04.tar    checkSum
 05.Chaetodon lunulatus   2534  536  fish_05.tar    checkSum  mask_05.tar    checkSum
 06.Chaetodon trifascialis   190  79  fish_06.tar    checkSum  mask_06.tar    checkSum
 07.Myripristis kuntee   450  71  fish_07.tar    checkSum  mask_07.tar    checkSum
 08.Acanthurus nigrofuscus   218  71  fish_08.tar    checkSum  mask_08.tar    checkSum
 09.Hemigymnus fasciatus   241  58  fish_09.tar    checkSum  mask_09.tar    checkSum
 10.Neoniphon sammara    299  53  fish_10.tar    checkSum  mask_10.tar    checkSum
 11.Abudefduf vaigiensis   98  42  fish_11.tar    checkSum  mask_11.tar    checkSum
 12.Canthigaster valentini   147  28  fish_12.tar    checkSum  mask_12.tar    checkSum
 13.Pomacentrus moluccensis   181  27  fish_13.tar    checkSum  mask_13.tar    checkSum
 14.Zebrasoma scopas   90  23  fish_14.tar    checkSum  mask_14.tar    checkSum
 15.Hemigymnus melapterus   42  16  fish_15.tar    checkSum  mask_15.tar    checkSum
 16.Lutjanus fulvus   206  15  fish_16.tar    checkSum  mask_16.tar    checkSum
 17.Scolopsis bilineata   49  8  fish_17.tar    checkSum  mask_17.tar    checkSum
 18.Scaridae    56  5  fish_18.tar    checkSum  mask_18.tar    checkSum
 19.Pempheris vanicolensis    29  6  fish_19.tar    checkSum  mask_19.tar    checkSum
 20.Zanclus cornutus   21  6  fish_20.tar    checkSum  mask_20.tar    checkSum
 21.Neoglyphidodon nigroris    16  8  fish_21.tar    checkSum  mask_21.tar    checkSum
 22.Balistapus undulatus    41  6  fish_22.tar    checkSum  mask_22.tar    checkSum
 23.Siganus fuscescens    25  6  fish_23.tar    checkSum  mask_23.tar    checkSum

Page created by Phoenix X. Huang, Bastiaan B. Boom and Robert B. Fisher. Permission is granted for anyone to copy, use, modify, or distribute this data and accompanying documents for any purpose, provided this copyright notice is retained and prominently displayed, along with a note saying that the original data are available from our web page and refering to [2]. The data and documents are distributed without any warranty, express or implied. As the data were acquired for research purposes only, they have not been tested to the degree that would be advisable in any important application. All use of these data is entirely at the user's own risk.

Acknowledgments: This research was funded by European Commission FP7 grant 257024, in the Fish4Knowledge project.

[1]. B. J. Boom, P. X. Huang, C. Spampinato, S. Palazzo, J. He, C. Beyan, E. Beauxis-Aussalet, J. van Ossenbruggen, G. Nadarajan, J. Y. Chen-Burger, D. Giordano, L. Hardman, F.-P. Lin, R. B. Fisher, "Long-term underwater camera surveillance for monitoring and analysis of fish populations", Proc. Int. Workshop on Visual observation and Analysis of Animal and Insect Behavior (VAIB), in conjunction with ICPR 2012, Tsukuba, Japan, 2012.
[2]. B. J. Boom, P. X. Huang, J. He, R. B. Fisher, "Supporting Ground-Truth annotation of image datasets using clustering", 21st Int. Conf. on Pattern Recognition (ICPR), 2012.

There have been ****** accesses since October 2013.

© 2013 Robert Fisher

Valid HTML 4.0!