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Multimedia Tools and Applications

, Volume 77, Issue 7, pp 7851–7863 | Cite as

Rediscover flowers structurally

  • Cheng Pang
  • Hongxun Yao
  • Xiaoshuai Sun
  • Sicheng Zhao
  • Wei Yu
Article
  • 103 Downloads

Abstract

Existing methods for flower classification are usually focused on segmentation of the foreground, followed by extraction of features. After extracting the features from the foreground, global pooling is performed for final classification. Although this pipeline can be applied to many recognition tasks, however, these approaches have not explored structural cues of the flowers due to the large variation in their appearances. In this paper, we argue that structural cues are essential for flower recognition. We present a novel approach that explores structural cues to extract features. The proposed method encodes the structure of flowers into the final feature vectors for classification by operating on salient regions, which is robust to appearance variations. In our framework, we first segment the flower accurately by refining the existing segmentation method, and then we generate local features using our approach. We combine our local feature with global-pooled features for classification. Evaluations on the Oxford Flower dataset shows that by introducing the structural cues and locally pooling of some off-the-shelf features, our method outperforms the state-of-the-arts which employ specific designed features and metric learning.

Keywords

Image classification Fine-grained classification Saliency detection Feature extraction 

References

  1. 1.
    Akata Z, Reed S, Walter D, Lee H, Schiele B (2015) Evaluation of output embeddings for fine-grained image classification. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2927–2936Google Scholar
  2. 2.
    Angelova A, Zhu S (2013) Efficient object detection and segmentation for fine-grained recognition. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 811–818Google Scholar
  3. 3.
    Angelova A, Zhu S, Lin Y (2013) Image segmentation for large-scale subcategory flower recognition. In: 2013 IEEE workshop on applications of computer vision (WACV). IEEE, pp 39–45Google Scholar
  4. 4.
    Angelova A, Zhu S, Lin Y, Wong J, Shpecht C (2012) Development and deployment of a large-scale flower recognition mobile app. NEC Labs America Technical ReportGoogle Scholar
  5. 5.
    Boykov YY, Jolly MP (2001) Interactive graph cuts for optimal boundary & region segmentation of objects in nd images. In: 2001 IEEE international conference on computer vision (ICCV). IEEE, pp 105–112Google Scholar
  6. 6.
    Branson S, Van Horn G, Wah C, Perona P, Belongie S (2014) The ignorant led by the blind: a hybrid human–machine vision system for fine-grained categorization. Int J Comput Vis 108(1–2):3–29Google Scholar
  7. 7.
    Britto Mottos A, Schmidt Feris R (2014) Fusing well-crafted feature descriptors for efficient fine-grained classification. In: 2014 IEEE international conference on image processing (ICIP). IEEE, pp 5197–5201Google Scholar
  8. 8.
    Chai Y, Lempitsky V, Zisserman A (2011) Bicos: a bi-level co-segmentation method for image classification. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 2579–2586Google Scholar
  9. 9.
    Cheng M, Mitra NJ, Huang X, Torr PH, Hu S (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3):569–582CrossRefGoogle Scholar
  10. 10.
    Guru D, Kumar YS, Manjunath S (2011) Textural features in flower classification. Math Comput Model 54(3):1030–1036CrossRefGoogle Scholar
  11. 11.
    Guru D, Sharath Y, Manjunath S (2010) Texture features and knn in classification of flower images. IJCA Special Issue on RTIPPR (1), pp 21–29Google Scholar
  12. 12.
    Kanan C, Cottrell G (2010) Robust classification of objects, faces, and flowers using natural image statistics. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2472–2479Google Scholar
  13. 13.
    Khan FS, Weijer J, Bagdanov AD, Vanrell M (2011) Portmanteau vocabularies for multi-cue image representation. In: Advances in neural information processing systems, pp 1323–1331Google Scholar
  14. 14.
    Krause J, Jin H, Yang J, Fei-Fei L (2015) Fine-grained recognition without part annotations. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 5546–5555Google Scholar
  15. 15.
    Lin TY, RoyChowdhury A, Maji S (2015) Bilinear cnn models for fine-grained visual recognition. In: 2015 IEEE international conference on computer vision (ICCV). IEEE, pp 1449–1457Google Scholar
  16. 16.
    Lu Y, Zhang W, Jin C, Xue X (2012) Learning attention map from images. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1067–1074Google Scholar
  17. 17.
    Mancas M, Mancas-Thillou C, Gosselin B, Macq B (2006) A rarity-based visual attention map-application to texture description. In: 2006 IEEE international conference on image processing (ICIP). IEEE, pp 445–448Google Scholar
  18. 18.
    Nilsback ME, Zisserman A (2007) Delving into the whorl of flower segmentation. BMVC, pp 1–10Google Scholar
  19. 19.
    Nilsback ME, Zisserman A (2008) Automated flower classification over a large number of classes. In: Sixth Indian conference on computer vision, graphics & image processing, 2008. ICVGIP’08. IEEE, pp 722–729Google Scholar
  20. 20.
    Nilsback ME, Zisserman A (2009) An automatic visual flora-segmentation and classification of flower images. Oxford UniversityGoogle Scholar
  21. 21.
    Parkhi OM, Vedaldi A, Jawahar C, Zisserman A (2011) The truth about cats and dogs. In: 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, pp 1427–1434Google Scholar
  22. 22.
    Parkhi OM, Vedaldi A, Zisserman A, Jawahar C (2012) Cats and dogs. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3498–3505Google Scholar
  23. 23.
    Rosch E, Mervis CB, Gray WD, Johnson DM, Boyes-Braem P (1976) Basic objects in natural categories. Cogn Psychol 8(3):382–439CrossRefGoogle Scholar
  24. 24.
    Vedaldi A, Zisserman A (2012) Efficient additive kernels via explicit feature maps. IEEE Trans Pattern Anal Mach Intell 34(3):480–492CrossRefGoogle Scholar
  25. 25.
    Wah C, Maji S, Belongie S (2015) Learning localized perceptual similarity metrics for interactive categorization. In: 2015 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 502–509Google Scholar
  26. 26.
    Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality-constrained linear coding for image classification. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3360–3367Google Scholar
  27. 27.
    Zhang N, Donahue J, Girshick R, Darrell T (2014) Part-based r-cnns for fine-grained category detection. In: European conference on computer vision (ECCV). Springer, pp 834–849Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

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