Texture Segmentation Benchmark

被引:10
作者
Mikes, Stanislav [1 ]
Haindl, Michal [1 ]
机构
[1] Czech Acad Sci, Inst Informat Theory & Automat, Prague 11720, Czech Republic
关键词
Image segmentation; Benchmark testing; Heuristic algorithms; Image color analysis; Satellites; Lighting; Time measurement; Benchmark; image segmentation; texture segmentation; (Un)supervised segmentation; segmentation criteria; scale; rotation and illumination invariants; COLOR IMAGE SEGMENTATION; UNSUPERVISED SEGMENTATION; MODEL;
D O I
10.1109/TPAMI.2021.3075916
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The Prague texture segmentation data-generator and benchmark (mosaic.utia.cas.cz) is a web-based service designed to mutually compare and rank (recently nearly 200) different static and dynamic texture and image segmenters, to find optimal parametrization of a segmenter and support the development of new segmentation and classification methods. The benchmark verifies segmenter performance characteristics on potentially unlimited monospectral, multispectral, satellite, and bidirectional texture function (BTF) data using an extensive set of over forty prevalent criteria. It also enables us to test for noise robustness and scale, rotation, or illumination invariance. It can be used in other applications, such as feature selection, image compression, query by pictorial example, etc. The benchmark's functionalities are demonstrated in evaluating several examples of leading previously published unsupervised and supervised image segmentation algorithms. However, they are used to illustrate the benchmark functionality and not review the recent image segmentation state-of-the-art.
引用
收藏
页码:5647 / 5663
页数:17
相关论文
共 86 条
  • [1] Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration
    Alpert, Sharon
    Galun, Meirav
    Brandt, Achi
    Basri, Ronen
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2012, 34 (02) : 315 - 327
  • [2] Andrearczyk V, 2017, Arxiv, DOI arXiv:1703.05230
  • [3] Contour Detection and Hierarchical Image Segmentation
    Arbelaez, Pablo
    Maire, Michael
    Fowlkes, Charless
    Malik, Jitendra
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2011, 33 (05) : 898 - 916
  • [4] Bampis CG, 2016, IEEE IMAGE PROC, P1254
  • [5] Color- and texture-based image segmentation using EM and its application to content-based image retrieval
    Belongie, S
    Carson, C
    Greenspan, H
    Malik, J
    [J]. SIXTH INTERNATIONAL CONFERENCE ON COMPUTER VISION, 1998, : 675 - 682
  • [6] Quantitative evaluation of color image segmentation results
    Borsotti, M
    Campadelli, P
    Schettini, R
    [J]. PATTERN RECOGNITION LETTERS, 1998, 19 (08) : 741 - 747
  • [7] Unsupervised performance evaluation of image segmentation
    Chabrier, Sebastien
    Emile, Bruno
    Rosenberger, Christophe
    Laurent, Helene
    [J]. EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING, 2006, 2006 (1)
  • [8] Color image segmentation: advances and prospects
    Cheng, HD
    Jiang, XH
    Sun, Y
    Wang, JL
    [J]. PATTERN RECOGNITION, 2001, 34 (12) : 2259 - 2281
  • [9] Christoudias CM, 2002, INT C PATT RECOG, P150, DOI 10.1109/ICPR.2002.1047421
  • [10] The Cityscapes Dataset for Semantic Urban Scene Understanding
    Cordts, Marius
    Omran, Mohamed
    Ramos, Sebastian
    Rehfeld, Timo
    Enzweiler, Markus
    Benenson, Rodrigo
    Franke, Uwe
    Roth, Stefan
    Schiele, Bernt
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 3213 - 3223