Reported Results on MPEG-7 Core Experiment CE-Shape-1 Test Set

Improving shape retrieval/matching:

The retrieval rate is measured by the so-called bull’s eye score. Every shape in the database is compared to all other shapes, and the number of shapes from the same class among the 40 most similar shapes is reported. The bull’s eye retrieval rate is the ratio of the total number of shapes from the same class to the highest possible number (which is 20 × 1400). Thus, the best possible rate is 100%.

TABLE I

Alg.	CSS [1]	Vis. Parts [2]	Shape Contexts [3]	Aligning Curves [4]	Distance Set [5]	Prob. Approach [10]	Chance Prob. [8]	Skeletal Context [19]	Gen. Model [9]	Optimized CSS [6]
Score	75.44%	76.45%	76.51%	78.16%	78.38%	79.19%	79.36%	79.92%	80.03%	81.12%
Alg.	Contour Seg. [11]	Multiscale Rep. [7]	Shape L’AneRouge [20]	Fixed Cor. [12]	Inner Distance [15]	IDSC [15]	Symbolic Rep. [18]	Hier. Procrustes [13]	Triangle Area [17]	Shape Tree [14]
Score	84.33%	84.93%	85.25%	85.40%	85.40%	85.40%	85.92%	86.35%	87.23%	87.70%
Alg.	ASC [25]	Perc. R. [26]	IDSC +LP [16]	CS Shape Sim [21]	CS Shape Sim [22]	IDSC+ Mutual Graph [23]	AIR [24]	Perc. R. +LCDP [26]	ASC +LCDP [25]	ASC +TPG Diffusion [27]
Score	88.30%	88.39%	91.61%	91.61%	93.32%	93.40%	93.67%	95.60%	95.96%	96.47%
Alg.	AIR +TPG Diffusion [27]
Score	99.99%

ABOVE ARE THE RETRIEVAL RATES (BULL’S EYE) OF DIFFERENT METHODS ON THE MPEG-7 DATA SET.

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[4] T. Sebastian, P. Klein, and B. Kimia, “On aligning curves,” IEEE Trans. PAMI, vol. 25, pp. 116–125, 2003.

[5] C. Grigorescu and N. Petkov, “Distance sets for shape filters and shape recognition,” IEEE Trans. on Image Processing, vol. 12, no. 7, pp. 729–739, 2003.

[6] F. Mokhtarian and M. Bober, Curvature Scale Space Representation: Theory, Applications & MPEG-7 Standardization. Dordrecht: Kluwer Academic Publishers, 2003.

[7] T. Adamek and N. O’Connor, “A multiscale representation method for nonrigid shapes with a single closed contour,” IEEE Trans. on CSVT, vol. 14, no. 5, pp. 742–753, 2004.

[8] B. Super, “Learning chance probability functions for shape retrieval or classification,” in Proceedings of the IEEE Workshop on Learning in CVPR, 2004.

[9] Z. Tu and A. L. Yuille, “Shape matching and recognition - using generative models and informative features,” in ECCV, 2004, pp. 195–209.

[10] G. McNeill and S. Vijayakumar, “2d shape classification and retrieval,” in IJCAI, 2005.

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[14] P. F. Felzenszwalb and J. Schwartz, “Hierarchical matching of deformable shapes.” in CVPR, 2007.

[15] H. Ling and D. Jacobs, “Shape Classification Using the InnerDistance,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 2, pp. 286–299, Feb. 2007.

[16] D. Zhou, J. Huang, and B. Schlkopf, “Learning with Hypergraphs: Clustering, Classification, and Embedding,” Proc. Advances in Neural Information Processing Systems, 2007.

[17] N. Alajlan, M. Kamel, and G. Freeman, “Geometry-based image retrieval in binary image databases,” IEEE Trans. on PAMI, vol. 30, no. 6, pp. 1003–1013, 2008.

[18] M. Daliri and V. Torre, “Robust symbolic representation for shape recognition and retrieval,” Pattern Recognition, vol. 41, no. 5, pp. 1799–1815, 2008.

[19] J. Xie, P. Heng, and M. Shah, “Shape matching and modeling using skeletal context,” Pattern Recognition, vol. 41, no. 5, pp. 1756–1767, 2008.

[20] A. Peter, A. Rangarajan, and J. Ho, “Shape l’ˆane rouge: Sliding wavelets for indexing and retrieval,” in CVPR, 2008.

[21] Xiang Bai, Xingwei Yang, Longin Jan Latecki, Wenyu Liu, Zhuowen Tu. Learning Context Sensitive Shape Similarity by Graph Transduction. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI) 2009.

[22] Xingwei Yang, Suzan Koknar-Tezel, and Longin Jan Latecki. Locally Constrained Diffusion Process on Locally Densified Distance Spaces with Applications to Shape Retrieval. CVPR 2009.

[23] P. Kontschieder, M. Donoser, and H. Bischof, “Beyond Pairwise Shape Similarity Analysis,” Proc. Ninth Asian Conf. Computer Vision , 2009.

[24] R. Gopalan, P. Turaga, and R. Chellappa, “Articulation-Invariant Representation of Non-Planar Shapes,” Proc. European Conf. Computer Vision, 2010.

[25] H. Ling, X. Yang, and L.J. Latecki, “Balancing Deformability and Discriminability for Shape Matching,” Proc. 11th European Conf. Computer Vision, 2010.

[26] A. Temlyakov, B.C. Munsell, J.W. Waggoner1, and S. Wang, “Two Perceptually Motivated Strategies for Shape Classification,” Proc. IEEE Computer Vision and Pattern Recognition, 2010.

[27] Xingwei Yang, Lakshman Prasad, and Longin Jan Latecki. Affinity Learning with Diffusion on Tensor Product Graph. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 35, No. 1, pp. 28–38, January 2013.