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Aligning shapes for symbol classification and retrieval

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This paper proposes a method able to exploit peculiarities of both, local and global shape descriptors, to be employed for shape classification and retrieval. In the proposed framework, the silhouettes of symbols are firstly described through Bags of Shape Contexts. The shape signature is then used to solve the correspondence problem between points of two shapes. The obtained correspondences are employed to recover the geometric transformations between the shape to be classified/retrieved and the ones belonging to the training dataset. The alignment is based on a voting procedure in the parameter space of the model considered to recover the geometric transformation. The aligned shapes are finally described with the Blurred Shape Model descriptor for classification and retrieval purposes. Experimental results demonstrate the effectiveness of the proposed solution on two classic benchmark shape datasets, as well as on a large scale set of hand sketches composed by 20,000 examples distributed over 250 object categories.

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  1. 1.

    Azzaro G, Caccamo M, Ferguson J, Battiato S, Farinella GM, Guarnera G, Puglisi G, Petriglieri R, Licitra G (2011) Objective estimation of body condition score by modeling cow body shape from digital images. J Dairy Sci 94(4):2126–2137

  2. 2.

    Bai X, Liu W, Tu Z (2009) Integrating contour and skeleton for shape classification. In: IEEE international conference on computer vision workshops, pp 360–367

  3. 3.

    Battiato S, Farinella GM, Giudice O, Puglisi G (2012) Aligning bags of shape contexts for blurred shape model based symbol classification. In: Proceedings of international conference on pattern recognition (ICPR), pp 1598–1601

  4. 4.

    Battiato S, Farinella GM, Messina E, Puglisi G (2012) Robust image alignment for tampering detection. IEEE Trans Inf Forensics Secur 7(4):1105–1117

  5. 5.

    Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intel 24(4):509–522

  6. 6.

    Caglar T, Berrin Y, Metin TS (2012) Sketched symbol recognition with auto-completion. Pattern Recog 45:3926–3937

  7. 7.

    da Fontoura Costa L, Cesar RM Jr (2009) Shape classification and analysis: theory and practice, 2nd edn. CRC Press, Inc., Boca Raton

  8. 8.

    Daliri MR, Torre V (2008) Robust symbolic representation for shape recognition and retrieval. Pattern Recog 41(5):1782–1798

  9. 9.

    Eitz M, Hays J, Alexa M (2012) How do humans sketch objects?. ACM Trans Graph (SIGGRAPH) 31(4):44:1–44:10

  10. 10.

    Escalera S, Fornés A, Pujol O, Lladós J, Radeva P (2011) Circular blurred shape model for multiclass symbol recognition. IEEE Trans Syst Man Cybern 41(2):497–506

  11. 11.

    Farinella GM, Impoco G, Gallo G, Spoto S, Catanuto G, Nava MB (2006) Objective outcome evaluation of breast surgery. In: Medical image computing and computer-assisted intervention—MICCAI 2006. Lecture Notes in Computer Science, vol 4190. Springer, Berlin Heidelberg, pp 776–783

  12. 12.

    Kuhn HW (1955) The hungarian method for the assignment problem. Nav Res Logist Q 2:83–97

  13. 13.

    Lim KL, Galoogahi H (2010) Shape classification using local and global features. In: Pacific-rim symposium on image and video technology, pp 115–120

  14. 14.

    Marr D (1982) Vision: a computational investigation into the human representation and processing of visual information. Henry Holt and Co., Inc., New York

  15. 15.

    McNeill G, Vijayakumar S (2006) Hierarchical procrustes matching for shape retrieval. In: IEEE computer society conference on computer vision and pattern recognition, pp 885–894

  16. 16.

    Munder S, Schnörr C, Gavrila DM (2008) Pedestrian detection and tracking using a mixture of view-based shape-texture models. IEEE Trans Intell Transp Syst 9(2):333–343

  17. 17.

    Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2007) Object retrieval with large vocabularies and fast spatial matching. In: Conference on computer vision and pattern recognition

  18. 18.

    Puglisi G, Battiato S (2011) A robust image alignment algorithm for video stabilization purposes. IEEE Trans Circ Syst Video Technol 21(10):1390–1400

  19. 19.

    Qi GJ, Tian Q, Huang T (2011) Locality-sensitive support vector machine by exploring local correlation and global regularization. In: IEEE international conference on computer vision and pattern recognition, pp 841–848

  20. 20.

    Qi J, Xin F, Zhongxuan L, Yu L, He G (2014) A new geometric descriptor for symbols with affine deformations. Pattern Recogn Lett 40:128–135

  21. 21.

    Velasco-Forero S, Angulo J (2010) Statistical shape modeling using morphological representations. In: International conference on pattern recognition, pp 3537–3540

  22. 22.

    Wang B, Shen W, Liu WY, You XG, Bai X (2010) Shape classification using tree-unions. In: International conference on pattern recognition, pp 983–986

  23. 23.

    Wang J, Bai X, You X, Liu W, Latecki L (2011) Shape matching and classification using height functions. Pattern Recogn Lett 33:134–143

  24. 24.

    Yan Y, Shen H, Liu G, Ma Z, Gao C, Sebe N (2014) GLocal tells you more: coupling GLocal structural for feature selection with sparsity for image and video classification. Comp Vision Image Underst 124:99–109. Large Scale Multimedia Semantic Indexing

  25. 25.

    Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recog 37(1):1–19

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Correspondence to Oliver Giudice.

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Battiato, S., Farinella, G.M., Giudice, O. et al. Aligning shapes for symbol classification and retrieval. Multimed Tools Appl 75, 5513–5531 (2016). https://doi.org/10.1007/s11042-015-2523-7

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  • Shape recognition
  • Shape retrieval
  • Symbol classification
  • Alignment