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BY-NC-ND 3.0 license Open Access Published by De Gruyter Open Access May 16, 2016

Local 2D Pattern Spectra as Connected Region Descriptors

  • Petra Bosilj , Michael H. F. Wilkinson , Ewa Kijak and Sébastien Lefèvre


We validate the usage of augmented 2D shape-size pattern spectra, calculated on arbitrary connected regions. The evaluation is performed on MSER regions and competitive performance with SIFT descriptors achieved in a simple retrieval system, by combining the local pattern spectra with normalized central moments. An additional advantage of the proposed descriptors is their size: being half the size of SIFT, they can handle larger databases in a time-efficient manner. We focus in this paper on presenting the challenges faced when transitioning from global pattern spectra to the local ones. An exhaustive study on the parameters and the properties of the newly constructed descriptor is offered, as well as performance results from preliminary experiments, validating the usage of the descriptor. We also consider possible improvements to the quality and computation efficiency of the proposed local descriptors.


[1] R. Arandjelovic and A. Zisserman. Three things everyone should know to improve object retrieval. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2911–2918. IEEE, 2012. 10.1109/CVPR.2012.6248018Search in Google Scholar

[2] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool. Speeded-up robust features (SURF). Computer vision and image understanding, 110(3):346–359, 2008. 10.1016/j.cviu.2007.09.014Search in Google Scholar

[3] J. A. Benediktsson, M. Pesaresi, and K. Arnason. Classification and Feature Extraction for Remote Sensing Images from Urban Areas based on Morphological Transformations. IEEE Transactions on Geoscience and Remote Sensing, 41(9):1940–1949, 2003. 10.1109/TGRS.2003.814625Search in Google Scholar

[4] P. Bosilj, E. Kijak, M. H. F. Wilkinson, and S. Lefèvre. Short local descriptors from 2D connected pattern spectra. To appear in ICIP 2015. 10.1109/ICIP.2015.7351060Search in Google Scholar

[5] Petra Bosilj, Michael HFWilkinson, Ewa Kijak, and Sébastien Lefèvre. Local 2d pattern spectra as connected region descriptors. In Mathematical Morphology and Its Applications to Signal and Image Processing, pages 182–193. Springer, 2015. 10.1007/978-3-319-18720-4_16Search in Google Scholar

[6] E. J. Breen and R. Jones. Attribute openings, thinnings, and granulometries. Computer Vision and Image Understanding, 64(3):377–389, 1996. 10.1006/cviu.1996.0066Search in Google Scholar

[7] K. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone. Morphological Attribute Profiles for the Analysis of Very High Resolution Images. IEEE Transactions on Geoscience and Remote Sensing, 48(10):3747–3762, 2010. 10.1109/TGRS.2010.2048116Search in Google Scholar

[8] Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni. Locality-sensitive Hashing Scheme Based on P-stable Distributions. In Proceedings of the Twentieth Annual Symposium on Computational Geometry, SCG ’04, pages 253–262, 2004. 10.1145/997817.997857Search in Google Scholar

[9] J. H. Friedman, J. L. Bentley, and R. A. Finkel. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software (TOMS), 3(3):209–226, 1977. 10.1145/355744.355745Search in Google Scholar

[10] M.-K. Hu. Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on, 8(2):179–187, 1962. 10.1109/TIT.1962.1057692Search in Google Scholar

[11] R. Jones. Component trees for image filtering and segmentation. In IEEE Workshop on Nonlinear Signal and Image Processing, E. Coyle, Ed., Mackinac Island, 1997. Search in Google Scholar

[12] Y. Ke and R. Sukthankar. PCA-SIFT: A more distinctive representation for local image descriptors. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, volume 2, pages II– 506. IEEE, 2004. Search in Google Scholar

[13] H. Lejsek, B. Þ. Jónsson, and L. Amsaleg. NV-Tree: Nearest Neighbors at the Billion Scale. In Proceedings of the 1st ACM International Conference on Multimedia Retrieval, ICMR ’11, pages 54:1–54:8, 2011. Search in Google Scholar

[14] D. G. Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91– 110, 2004. 10.1023/B:VISI.0000029664.99615.94Search in Google Scholar

[15] P. Maragos. Pattern spectrum and multiscale shape representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 11(7):701–716, 1989. 10.1109/34.192465Search in Google Scholar

[16] J. Matas, O. Chum, M. Urban, and T. Pajdla. Robust wide-baseline stereo from maximally stable extremal regions. Image and vision computing, 22(10):761–767, 2004. 10.1016/j.imavis.2004.02.006Search in Google Scholar

[17] K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(10):1615–1630, 2005. 10.1109/TPAMI.2005.188Search in Google Scholar PubMed

[18] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool. A comparison of affine region detectors. International journal of computer vision, 65(1-2):43–72, 2005. 10.1007/s11263-005-3848-xSearch in Google Scholar

[19] M. Muja and D. G. Lowe. Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration. In International Conference on Computer Vision Theory and Application VISSAPP’09), pages 331–340. INSTICC Press, 2009. Search in Google Scholar

[20] E. Mwebaze, P. Schneider, F.-M. Schleif, J. R. Aduwo, J. A. Quinn, S. Haase, T. Villmann, and M. Biehl. Divergence-based classification in learning vector quantization. Neurocomputing, 74(9):1429–1435, 2011. 10.1016/j.neucom.2010.10.016Search in Google Scholar

[21] D. Nistér and H. Stewénius. Linear timemaximally stable extremal regions. In Computer Vision–ECCV 2008, pages 183–196. Springer, 2008. 10.1007/978-3-540-88688-4_14Search in Google Scholar

[22] G. K. Ouzounis, M. Pesaresi, and P. Soille. Differential Area Profiles: Decomposition Properties and Efficient Computation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(8):1533–1548, 2012. Search in Google Scholar

[23] P. Salembier, A. Oliveras, and L. Garrido. Antiextensive connected operators for image and sequence processing. Image Processing, IEEE Transactions on, 7(4):555–570, 1998. 10.1109/83.663500Search in Google Scholar PubMed

[24] G. Schaefer and M. Stich. UCID: An Uncompressed Colour Image Database. In Electronic Imaging 2004, pages 472–480. International Society for Optics and Photonics, 2003. 10.1117/12.525375Search in Google Scholar

[25] C. Schmid and R. Mohr. Object recognition using local characterization and semi-local constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5):530–534, 1997. 10.1109/34.589215Search in Google Scholar

[26] J. Sivic and A. Zisserman. Video Google: Efficient visual search of videos. In J. Ponce, M. Hebert, C. Schmid, and A. Zisserman, editors, Toward Category-Level Object Recognition, volume 4170 of LNCS, pages 127–144. Springer, 2006. 10.1007/11957959_7Search in Google Scholar

[27] F. Tushabe and M. H. F. Wilkinson. Content-based image retrieval using combined 2D attribute pattern spectra. In Advances in Multilingual and Multimodal Information Retrieval, pages 554–561. Springer, 2008. 10.1007/978-3-540-85760-0_69Search in Google Scholar

[28] E. R. Urbach, J. B. T. M. Roerdink, and M. H. F. Wilkinson. Connected shape-size pattern spectra for rotation and scaleinvariant classification of gray-scale images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 29(2):272– 285, 2007. 10.1109/TPAMI.2007.28Search in Google Scholar PubMed

[29] E. R. Urbach and M. H. F. Wilkinson. Shape-only granulometries and grey-scale shape filters. In Proc. Int. Symp. Math. Morphology (ISMM), volume 2002, pages 305–314, 2002. Search in Google Scholar

[30] A. Vedaldi and B Fulkerson. VLFeat: An Open and Portable Library of Computer Vision Algorithms., 2008. Search in Google Scholar

[31] M. A. Westenberg, J. B. T. M. Roerdink, and M. H. F. Wilkinson. Volumetric Attribute Filtering and Interactive Visualization using the Max-Tree Representation. IEEE Trans. Image Proc., 16:2943–2952, 2007. 10.1109/TIP.2007.909317Search in Google Scholar

[32] M. H. F. Wilkinson. Generalized pattern spectra sensitive to spatial information. In Pattern Recognition, International Conference on, volume 1, pages 10021–10021. IEEE Computer Society, 2002. Search in Google Scholar

[33] Y. Xu, T. Géraud, and L. Najman. Morphological filtering in shape spaces: Applications using tree-based image representations. In Pattern Recognition (ICPR), 2012 21st International Conference on, pages 485–488. IEEE, 2012. Search in Google Scholar

Received: 2015-7-20
Accepted: 2016-2-11
Published Online: 2016-5-16

© 2016 Petra Bosilj et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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